Campbell et al v. Facebook Inc.

Filing 138

MOTION to Certify Class filed by Matthew Campbell, Michael Hurley. Motion Hearing set for 3/16/2016 09:00 AM in Courtroom 3, 3rd Floor, Oakland before Hon. Phyllis J. Hamilton. Responses due by 1/15/2016. Replies due by 2/19/2016. (Attachments: # 1 Declaration of Michael W. Sobol, # 2 Declaration of Hank Bates, # 3 Declaration of David T. Rudolph, # 4 Declaration of Melissa Gardner, # 5 Proposed Order)(Sobol, Michael) (Filed on 11/13/2015)

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Michael W. Sobol (State Bar No. 194857)
[e-mail]
David T. Rudolph (State Bar No. 233457)
[e-mail]
Melissa Gardner (State Bar No. 289096)
[e-mail]
LIEFF CABRASER HEIMANN & BERNSTEIN, LLP
275 Battery Street, 29th Floor
San Francisco, CA 94111-3339
Telephone: [phone]
Facsimile: 415.956.1008

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Hank Bates (State Bar No. 167688)
[e-mail]
Allen Carney
[e-mail]
David Slade
[e-mail]
CARNEY BATES & PULLIAM, PLLC
11311 Arcade Drive
Little Rock, AR 72212
Telephone: [phone]
Facsimile: 501.312.8505

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Attorneys for Plaintiffs and the Proposed Class

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UNITED STATES DISTRICT COURT
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NORTHERN DISTRICT OF CALIFORNIA
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OAKLAND DIVISION
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MATTHEW CAMPBELL and MICHAEL
HURLEY, on behalf of themselves and all
others similarly situated,

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Plaintiff,

Case No. C 13-05996 PJH (MEJ)
DECLARATION OF MELISSA GARDNER
IN SUPPORT OF PLAINTIFFS’ MOTION
FOR CLASS CERTIFICATION

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v.
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FACEBOOK, INC.,
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Date:
Time:
Judge:
Place:

March 16, 2016
9:00 a.m.
Hon. Phyllis J. Hamilton
Courtroom 3, 3rd Floor

Defendant.
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DECLARATION OF MELISSA GARDNER IN
SUPPORT OF MOTION FOR CLASS CERTIFICATION
CASE NO. 13-CV-05996-PJH (MEJ)

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I, Melissa Gardner, declare:

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1.

I am an attorney in the law firm of Lieff, Cabraser, Heimann & Bernstein, LLP, a

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member of the State Bar of California, and am admitted to practice before the United States

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District Court for the Northern District of California. I am one of the counsel for Plaintiffs in this

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action. I make this declaration based on my own personal knowledge. If called upon to testify, I

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could and would testify competently to the truth of the matters stated herein.

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2.

I submit this Declaration in support of Plaintiffs’ Motion for Class Certification.

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3.

Attached hereto as Exhibit 1 is a true and correct copy of excerpts from the

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transcript of the hearing held before the Honorable Phyllis Hamilton on October 1, 2014.
4.

Attached hereto as Exhibit 2 is a true and correct copy of the Expert Report of

Jennifer Golbeck in Support of Plaintiffs’ Motion for Class Certification.
5.

Attached hereto as Exhibit 3 is a true and correct copy of Facebook’s

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Supplemental Responses and Objections to Plaintiffs’ First Set of Interrogatories, which was

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served o September 8, 2015.

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6.

Attached hereto as Exhibit 4 is a true and correct copy of a document starting with

Bates stamp number FB000005502-R, which Facebook produced in this action.
7.

Attached hereto as Exhibit 5 are true and correct copies of excerpts from the

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September 25, 2015 deposition of Ray He in his personal capacity and in his capacity as a

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designee under Federal Rule of Civil Procedure 30(b)(6).

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8.

Attached hereto as Exhibit 6 is a true and correct copy of a document starting with

Bates stamp number FB000008489, which Facebook produced in this action.
9.

Attached hereto as Exhibit 7 is a true and correct copy of a document starting with

Bates stamp number FB000003118, which Facebook produced in this action.
10.

Attached hereto as Exhibit 8 is a true and correct copy of a document starting with

Bates stamp number FB000014365, which Facebook produced in this action.
11.

Attached hereto as Exhibit 9 is a true and correct copy of a document starting with

Bates stamp number FB000003335, which Facebook produced in this action.

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DECLARATION OF MELISSA GARDNER IN
SUPPORT OF MOTION FOR CLASS CERTIFICATION
CASE NO. 13-CV-05996-PJH (MEJ)

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12.

Attached hereto as Exhibit 10 is a true and correct copy of a document starting

with Bates stamp number FB000004996, which Facebook produced in this action.
13.

Attached hereto as Exhibit 11 is a true and correct copy of a document starting

with Bates stamp number FB000012539, which Facebook produced in this action.
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Attached hereto as Exhibit 12 is a true and correct copy of a document designated

FB000008268, which Facebook produced as a native file in this action.
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Attached hereto as Exhibit 13 is a true and correct copy of a document starting

with Bates stamp number FB000008722, which Facebook produced in this action.
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Attached hereto as Exhibit 14 is a true and correct copy of a document starting

with Bates stamp number FB000000594, which Facebook produced in this action.
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Attached hereto as Exhibit 15 is a true and correct copy of a document starting

with Bates stamp number FB000008304, which Facebook produced in this action.
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Attached hereto as Exhibit 16 is a true and correct copy of a document starting

with Bates stamp number FB000001265, which Facebook produced in this action.
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Attached hereto as Exhibit 17 is a true and correct copy of a document starting

with Bates stamp number FB000006429, which Facebook produced in this action.
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Attached hereto as Exhibit 18 is a true and correct copy of a document starting

with Bates stamp number FB000008271, which Facebook produced in this action.
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Attached hereto as Exhibit 19 is a true and correct copy of a PowerPoint

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presentation entitled Quarterly Earnings Slide Q4 2012 by Facebook, Inc., available online, at:

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http://files.shareholder.com/downloads/AMDA-NJ5DZ/2297890522x0x631721/fc91bd68-c60f-

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46c0-b3d4-f26455e115f7/FB_Q412_InvestorDeck.pdf.

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Attached hereto as Exhibit 20 is a true and correct copy of Defendant Facebook,

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Inc.’s Supplemental Responses and Objections to Plaintiffs’ Narrowed Second Set of

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Interrogatories, which, as Exhibit 1 thereto attaches a chart identifying documents produced by

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Defendant in this action associated with a selection of the private messages sent by each of the

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proposed Class Representatives, as well as the sender, recipient, date, time, and URL associated

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with each message.
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DECLARATION OF MELISSA GARDNER IN
SUPPORT OF MOTION FOR CLASS CERTIFICATION
CASE NO. 13-CV-05996-PJH (MEJ)

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Attached hereto as Exhibit 21 is a true and correct copy of a document starting

with Bates stamp number FB000000001, which Facebook produced in this action.
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Attached hereto as Exhibit 22 is a true and correct copy of a document starting

with Bates stamp number FB000000032, which Facebook produced in this action.
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Attached hereto as Exhibit 23 is a true and correct copy of a document starting

with Bates stamp number FB000000058, which Facebook produced in this action.
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Attached hereto as Exhibit 24 is a true and correct copy of a document starting

with Bates stamp number FB000000011, which Facebook produced in this action.
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Attached hereto as Exhibit 25 is a true and correct copy of a document starting

with Bates stamp number FB000000017, which Facebook produced in this action.
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Attached hereto as Exhibit 26 is a true and correct copy of a document starting

with Bates stamp number FB000000043, which Facebook produced in this action.
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Attached hereto as Exhibit 27 is a true and correct copy of a document starting

with Bates stamp number FB000006435, which Facebook produced in this action.
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Attached hereto as Exhibit 28 is a true and correct copy of a document starting

with Bates stamp number FB000004406, which Facebook produced in this action.
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Attached hereto as Exhibit 29 is a true and correct copy of a document starting

with Bates stamp number FB000007924, which Facebook produced in this action.
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Attached hereto as Exhibit 30 is a true and correct copy of a document starting

with Bates stamp number FB000000502, which Facebook produced in this action.
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Attached hereto as Exhibit 31 is a true and correct copy of Plaintiffs’ First Set of

Requests for Production of Documents to Defendant.
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Attached hereto as Exhibit 32 is a true and correct copy of a letter from

Facebook’s counsel Joshua Jessen to Plaintiffs’ counsel Hank Bates, dated April 10, 2015.
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Attached hereto as Exhibit 33 is a true and correct copy of the Expert Report of

Fernando Torres in Support of Plaintiffs’ Motion for Class Certification.
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Attached hereto as Exhibit 34 is a true and correct copy of a document starting

with Bates stamp number FB00000802, which Facebook produced in this action.
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DECLARATION OF MELISSA GARDNER IN
SUPPORT OF MOTION FOR CLASS CERTIFICATION
CASE NO. 13-CV-05996-PJH (MEJ)

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I declare under penalty of perjury that the foregoing is true and correct and that this
Declaration was signed in San Francisco, California, on November 13, 2015.

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LIEFF CABRASER HEIMANN & BERNSTEIN, LLP

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By:

/s/Melissa Gardner
Melissa Gardner

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DECLARATION OF MELISSA GARDNER IN
SUPPORT OF MOTION FOR CLASS CERTIFICATION
CASE NO. 13-CV-05996-PJH (MEJ)

EXHIBIT 1

UNITED STATES DISTRICT COURT CERTIFIED COPY
NORTHERN DISTRICT OF CALIFORNIA

BEFORE THE HONORABLE PHYLLIS J. HAMILTON, JUDGE

MATTHEW CAMPBELL, MICHAEL
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HURLEY, AND DAVID SHADPOUR, )
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PLAINTIFFS,
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VS.
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FACEBOOK, INC.,
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DEFENDANT.
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____________________________)

MOTION TO DISMISS
PAGES 1 - 77
NO. C 13-05996PJH

OAKLAND, CALIFORNIA
WEDNESDAY, OCTOBER 1, 2014

REPORTER'S TRANSCRIPT OF PROCEEDINGS
APPEARANCES:
FOR PLAINTIFFS:
BY:

BY:

LIEFF CABRASER HEIMANN & BERNSTEIN LLP
275 BATTERY STREET, 30TH FLOOR
SAN FRANCISCO, CALIFORNIA 94111
MELISSA A. GARDNER,
MICHAEL W. SOBOL, ATTORNEY AT LAW
LIEFF CABRASER HEIMANN & BERNSTEIN LLP
780 THIRD AVENUE, 48TH FLOOR
NEW YORK, NEW YORK 10017-2024
NICHOLAS R. DIAMAND, ATTORNEY AT LAW

(APPEARANCES CONTINUED NEXT PAGE)
REPORTED BY:

RAYNEE H. MERCADO, CSR NO. 8258

PROCEEDINGS REPORTED BY ELECTRONIC/MECHANICAL STENOGRAPHY;
TRANSCRIPT PRODUCED BY COMPUTER-AIDED TRANSCRIPTION.
RAYNEE H. MERCADO, CSR, RMR, CRR, FCRR [phone]

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MR. JESSEN:

SURE, YOUR HONOR.

IF -- IF I CAN PUT THIS CASE INTO CONTEXT, TO BEGIN WITH,

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THIS IS A CASE -- THE CONSOLIDATED AMENDED COMPLAINT

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CHALLENGES ROUTINE COMMERCIAL CONDUCT THAT WAS COMPLETELY

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INNOCUOUS THAT PLAINTIFFS ADMIT CEASED OVER TWO YEARS AGO,

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AROUND OCTOBER OF 2012.

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THE REASON THERE WAS A 15-MONTH DELAY BETWEEN FILING OF

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THE FIRST COMPLAINT IN THIS CASE AND THE CESSATION OF THE

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CONDUCT WAS VERY SIMPLE.

THIS IS A COPY-CAT LAWSUIT.

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THE COURT:

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WHAT SPECIFIC CONDUCT CEASED?

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MR. JESSEN:

WHEN YOU SAY "THE CESSATION OF CONDUCT,"

YEAH, WELL, THE CONDUCT THAT CEASED

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WAS -- AND I'M HAPPY TO GET INTO THE DETAILS.

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NUMBER OF FACTORS THAT THERE ARE -- THERE -- FACEBOOK HAS

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SOCIAL PLUG-INS, WHICH PLAINTIFFS DISCUSS IN -- COMPLAINT, AND

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WE DISCUSS IN OUR BRIEF.

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THIRD-PARTY WEBSITES.

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WE MIGHT HAVE A NEW YORK TIMES TRAVEL ARTICLE THAT HAS THE

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PARTICULAR SOCIAL PLUG-IN.

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AND THESE SOCIAL PLUG-INS APPEAR ON

SO AN EXAMPLE WE GIVE IN OUR MOTION IS

IF YOUR HONOR'S BROWSING THE --

(OFF-THE-RECORD DISCUSSION.)

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THE COURT:

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MR. JESSEN:

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THERE ARE A

SLOW DOWN.
UNDERSTOOD.

LOTS OF DIFFERENT TECHNOLOGY COMPANIES HAVE SOCIAL

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PLUG-INS, OKAY, FACEBOOK AMONG THEM.

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EXAMPLE, A NEW YORK TIMES TRAVEL ARTICLE, IT MIGHT HAVE THE

SO IF YOU GO TO,

RAYNEE H. MERCADO, CSR, RMR, CRR, FCRR [phone]

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FACEBOOK SOCIAL PLUG-IN, WHICH CAN TAKE DIFFERENT FORMS, ONE

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OF WHICH IS THE "LIKE" BUTTON.

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PLUG-IN WILL HAVE A NUMBER NEXT TO IT, AND THAT IS THE NUMBER

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OF PEOPLE WHO HAVE "LIKED" THIS PARTICULAR -- THIS PARTICULAR

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WEB PAGE.

OFTENTIMES, THAT SOCIAL

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PRIOR TO OCTOBER OF 2012, ONE OF THE THINGS THAT WAS

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INCLUDED IN THAT ANONYMOUS AGGREGATE NUMBER WAS IF A FACEBOOK

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USER SENT A MESSAGE ON THE FACEBOOK PLATFORM TO ANOTHER

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FACEBOOK USER AND INCLUDED A -- A URL, A LINK TO THAT WEBSITE,

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THEN THE -- THE COUNT ON THAT WEBSITE WOULD GO UP.

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NOW, THERE ARE OTHER THINGS, OF COURSE, THAT GO INTO THAT.

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IF SOMEONE AFFIRMATIVELY IS ON THE SITE AND AFFIRMATIVELY

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CLICKS "LIKE," THAT INCREASES IT.

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WITH YOUR FRIENDS -- SO THERE WERE DIFFERENT -- THERE ARE

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DIFFERENT FACTORS THAT --

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THE COURT:

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MR. JESSEN:

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THE COURT:

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IF YOU SHARE THAT ON --

SO THE "LIKE" NUMBER WOULD INCREASE -CORRECT.
-- ONCE IT'S SENT BY A FACEBOOK USER

AND --

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MR. JESSEN:

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THE COURT:

CORRECT.
-- TO A RECIPIENT, IT WOULD INCREASE BY

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ONE?

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IT, IT WOULD INCREASE BY ANOTHER?

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AND THEN IF THE PERSON -- IF THE RECIPIENT CLICKED ON

MR. JESSEN:

I BELIEVE THAT'S CORRECT, YOUR HONOR.

THERE ARE MULTIPLE THINGS THAT GO INTO IT -- NOW, THERE
RAYNEE H. MERCADO, CSR, RMR, CRR, FCRR [phone]

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WAS A -- THERE'S SOME DISCUSSION OF THIS IN THE COMPLAINT.

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THERE WAS A BUG FOR A PERIOD OF TIME WHERE THE COUNT WAS

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ACTUALLY GOING UP BY TWO, BUT -- BUT PUTTING THE BUG ASIDE,

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YES, THAT WAS -- IF YOU INCLUDED THE URL IN THE MESSAGE, THIS

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ANONYMOUS AGGREGATE NUMBER, WHICH IS NOT LINKED TO A PERSON AT

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ALL, WENT UP.

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AND THAT'S THE CONDUCT, THAT'S -- THAT STOPPED AROUND

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OCTOBER OF 2012.

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ABOUT.

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NOW --

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AND THAT'S REALLY WHAT THIS -- THIS CASE IS

THE COURT:

MR. JESSEN:

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THE COURT:

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MR. JESSEN:

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THE COURT:

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THAT'S CORRECT, YOUR HONOR.
OKAY.

AFTER THE OCTOBER --

THAT'S CORRECT.

THE NUMBER --

FACEBOOK STOPPED COUNTING THEM IN THE

"LIKE" --

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THE CONDUCT THAT STOPPED IS THAT

THE NUMBERS WOULDN'T GO UP.

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WAIT.

MR. JESSEN:
MESSAGE.

CORRECT.

IN -- FOR -- FOR A SHARE IN A

THAT'S THE CONDUCT THAT STOPPED.

AND THAT'S REALLY -- MAYBE WE'LL HAVE SOME DISPUTE, BUT
THAT'S REALLY WHAT'S DRIVING THIS CASE.
PLAINTIFFS INITIALLY, AS YOUR HONOR -THE COURT:

BUT IS IT REALLY JUST THE NUMBER THAT

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APPEARS IN THE "LIKE" BUTTON THAT THE PLAINTIFFS ARE

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COMPLAINING ABOUT?

ISN'T IT ACTUALLY THE SCANNING -- AND I'M

RAYNEE H. MERCADO, CSR, RMR, CRR, FCRR [phone]

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NOT EXACTLY SURE WHAT THAT MEANS, AND I'M SURE SOMEONE WILL

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TELL ME -- OR REVIEW OF THE ACTUAL MESSAGE THAT WAS SENT FROM

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A FACEBOOK USER TO SOMEONE ELSE?

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ASKED YOU WHAT CONDUCT CEASED --

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MR. JESSEN:

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THE COURT:

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YES.
-- YOU'VE EXPLAINED THAT THE CONDUCT OF

COUNTING THAT TRANSMISSION AS A "LIKE" CEASED.

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MR. JESSEN:

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THE COURT:

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CORRECT.
BUT DID THE ACTUAL CONDUCT OF SCANNING OR

LOOKING AT THESE MESSAGES THAT ARE SENT STOP?

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ANYTHING THAT'S SHARED ON -- FACEBOOK IS

MR. JESSEN:

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A PLATFORM.

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SERVICE.

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SO MY QUESTION IS -- WHEN I

IT'S THE WORLD'S LARGEST SOCIAL NETWORKING

IT HAS OVER A BILLION USERS.

ANYTHING THAT IS SHARED ON THAT SITE IS BY DEFINITION

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ANALYZED BY COMPUTERS.

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ABOVE ALL OF WHICH ARE PROTECTING THE INTEGRITY OF THE SITE.

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IT HAS TO BE FOR A VARIETY OF REASONS,

AS YOU CAN IMAGINE, SUCH A LARGE PLATFORM IS SUBJECT TO

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ALL KIND OF ATTEMPTS TO HACK THE SITE, SERVE SPAM TO ITS

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USERS, MAL-WARE, SO ANY -- ANYTHING THAT'S SHARED ON THE SITE,

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YOUR HONOR, IS GOING TO BE SUBJECT TO AUTOMATIC -- AUTOMATED

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SYSTEMS THAT ARE DESIGNED TO FILTER SPAM, PROTECT THE

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INTEGRITY OF THE SITE, AND EVEN VERY BASIC --

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THE COURT:

SO THE ANSWER TO MY QUESTION IS NO, THAT

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THAT CONDUCT -- THAT THE PROCESS STILL EVALUATES THE -- THE --

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WHAT'S EITHER THE CONTENT OF OR WHAT'S ATTACHED TO THE
RAYNEE H. MERCADO, CSR, RMR, CRR, FCRR [phone]

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MESSAGES THAT ARE SENT.

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MR. JESSEN:

THERE -- THERE IS ANALYSIS THAT'S GOING

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ON.

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OF THE WIRETAP ACT OR ANY OTHER CRIMINAL STATUTE.

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AND --

WE DON'T -- WE DON'T THINK THAT THAT ANALYSIS RUNS AFOUL
BUT --

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AND AN IMPORTANT POINT TO BEAR IN MIND, YOUR HONOR, IS

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THEY'RE NOT CHALLENGING -- THEY'RE CHALLENGING A VERY SPECIFIC

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THING, WHICH WAS THEY SAY, FACEBOOK, YOU WERE USING THESE

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SHARES TO INCREASE AN -- AN ANONYMOUS NUMBER.

THEY'RE NOT

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CHALLENGING THAT VARIOUS PROCESSES HAVE TO TAKE PLACE ON

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THE -- ON THE PLATFORM TO PREVENT SPAM, TO PREVENT THINGS LIKE

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CHILD PORNOGRAPHY.

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THERE ARE SYSTEMS IN PLACE TO KEEP FACEBOOK AND ITS USERS

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SAFE AND SECURE.

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HONOR.

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COMPLAINING ABOUT THAT.

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SO, OF COURSE, THAT'S STILL GOING ON, YOUR

AND THEY'RE NOT -- THAT'S NOT -- THEY'RE NOT

AND THIS IS, I THINK, AN IMPORTANT POINT ON REALLY THE

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WIRETAP ACT CLAIM AND ALSO THE STATE LAW COROLLARY, WHICH IS

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631.

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THAT CLAIM FAILS AS A MATTER OF LAW.

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BEAR IN MIND, I THINK, IS THESE ARE CRIMINAL STATUTES THAT

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THEY'RE ASSERTING, PASSED IN -- INITIALLY IN (SIC) 1960'S AND

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THEN IN THE 1980'S, YEARS BEFORE THE WORLDWIDE WEB EVEN

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EXISTED.

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WE'VE LAID OUT A NUMBER OF REASONS WHY WE THINK THAT
ONE OVERARCHING THING TO

BUT THEY'RE CRIMINAL STATUTES.

AND UNDER CLEAR SUPREME COURT PRECEDENT, NINTH CIRCUIT
RAYNEE H. MERCADO, CSR, RMR, CRR, FCRR [phone]

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GET TO TO RESOLVE THE MOTION --

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THE COURT:

PERHAPS.

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MR. JESSEN:

"CONSENT" I THINK CAN BE RESOLVED AND,

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FRANKLY, "ORDINARY COURSE OF BUSINESS" BASED UPON THE LEGAL

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STANDARD AND WHAT THEY'VE ALLEGED IN THEIR COMPLAINT AND

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OBVIOUSLY ON THE 632 AND UCL.

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THE COURT:

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OKAY.

ALL RIGHT.

MATTER STANDS

SUBMITTED.
THANK YOU.

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MR. SOBOL:

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MR. JESSEN:

THANK YOU FOR YOUR PATIENCE, YOUR HONOR.
THANK YOU, YOUR HONOR.

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(PROCEEDINGS WERE CONCLUDED AT 11:05 A.M.)

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--O0O--

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CERTIFICATE OF REPORTER

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I CERTIFY THAT THE FOREGOING IS A CORRECT TRANSCRIPT

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FROM THE RECORD OF PROCEEDINGS IN THE ABOVE-ENTITLED MATTER.

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I FURTHER CERTIFY THAT I AM NEITHER COUNSEL FOR, RELATED TO,

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NOR EMPLOYED BY ANY OF THE PARTIES TO THE ACTION IN WHICH THIS

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HEARING WAS TAKEN, AND FURTHER THAT I AM NOT FINANCIALLY NOR

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OTHERWISE INTERESTED IN THE OUTCOME OF THE ACTION.

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___________________________________

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RAYNEE H. MERCADO, CSR, RMR, CRR, FCRR, CCRR

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SATURDAY, DECEMBER 20, 2014
RAYNEE H. MERCADO, CSR, RMR, CRR, FCRR [phone]

EXHIBIT 2

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Michael W. Sobol (State Bar No. 194857)
[e-mail]
David T. Rudolph (State Bar No. 233457)
[e-mail]
Melissa Gardner (State Bar No. 289096)
[e-mail]
LIEFF CABRASER HEIMANN & BERNSTEIN, LLP
275 Battery Street, 29th Floor
San Francisco, CA 94111-3339
Telephone: [phone]
Facsimile: 415.956.1008

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Hank Bates (State Bar No. 167688)
[e-mail]
Allen Carney
[e-mail]
David Slade
[e-mail]
CARNEY BATES & PULLIAM, PLLC
11311 Arcade Drive
Little Rock, AR 72212
Telephone: [phone]
Facsimile: 501.312.8505

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Attorneys for Plaintiffs and the Proposed Class

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UNITED STATES DISTRICT COURT

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NORTHERN DISTRICT OF CALIFORNIA

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MATTHEW CAMPBELL and MICHAEL
HURLEY, on behalf of themselves and all
others similarly situated,

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Plaintiffs,

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v.

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FACEBOOK, INC.,

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Defendant.

Case No. C 13-05996 PJH (MEJ)
REPORT OF DR. JENNIFER GOLBECK
IN SUPPORT OF PLAINTIFFS’ MOTION
FOR CLASS CERTIFICATION
HEARING
Date: March 16, 2016
Time: 9:00 a.m.
Place: Courtroom 3, 3rd Floor
|
The Honorable Phyllis J. Hamilton

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REPORT OF DR. GOLBECK IN SUPPORT OF
PLAINTIFFS’ MOTION FOR CLASS CERTIFICATION
C 13-05996 PJH (MEJ)

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TABLE OF CONTENTS

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Page

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I.
II.
III.

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IV.

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V.
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VI.
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VII.
VIII.
IX.

QUALIFICATIONS ............................................................................................... 1
METHODOLOGY AND SUMMARY OF CONCLUSIONS ............................... 3
FACEBOOK’S INTERCEPTION OF PRIVATE MESSAGE CONTENT........... 5
A.
Facebook’s Private Message Architecture Functionality............................ 5
B.
Facebook’s Interception and
of Private Message Content ........... 9
C.
Facebook’s Use Of Code-Based Devices To Intercept Private
Message Content ....................................................................................... 14
FACEBOOK’S USES OF INTERCEPTED PRIVATE MESSAGE DATA ....... 15
A.
Facebook Used Private Message Content To
And Other Features ............. 15
B.
Incrementing Like Counter ....................................................................... 23
FACEBOOK’S CONDUCT
........................ 26
A.
Facebook Has
........................................... 26
CLASS MEMBERS ARE ASCERTAINABLE ................................................... 27
A.
Class Members Can Be Identified Through
. ................ 27
THE CODE FOR ANALYZING PRIVATE MESSAGES OPERATED
THE SAME FOR ALL USERS ............................................................................ 29
“ORDINARY COURSE OF BUSINESS” .......................................................... 29
“IN TRANSMISSION” ........................................................................................ 32

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-i-

REPORT OF DR. GOLBECK IN SUPPORT OF
PLAINTIFFS’ MOTION FOR CLASS CERTIFICATION
C 13-05996 PJH (MEJ)

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I.

QUALIFICATIONS
1.

As indicated in my curriculum vitae, attached hereto as Exhibit A, I have been a

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professor in the College of Information Studies (“The iSchool”) at the University of Maryland

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since 2007 (assistant professor from 2007-2013, associate professor with tenure to present),

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where I have focused my research and teaching efforts on aspects of social media and the web.

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2.

I have been doing freelance professional web design and programming since 1993.

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I worked as a web designer for the University of Chicago from 1995-2000. I operated my own

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web design company, Gargoyle Web Design, from 1999-2001, which I closed when I began my

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Ph.D. work.

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3.

I have taught university level classes on the web and social media, including:

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“Analyzing Social Networks and Social Media,” “Social Networks: Technology and Society,”

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“Development of Internet Applications,” “Fundamentals of Human-Computer Interaction,”

13

“Information Users in Social Context,” and “Small Worlds, Social Networks, and Algorithms.” In

14

addition, I have published and presented many articles in refereed journals and conferences, and

15

over 100 of these relate to social media and the web.

16

4.

I received two Bachelor’s degrees, in Computer Science and Economics, from the

17

University of Chicago in 1999, a Master’s degree in Computer Science from the University of

18

Chicago in 2001, and a Ph.D. in Computer Science from the University of Maryland in 2005. My

19

Ph.D. thesis focused on social media and was titled “Computing and Applying Trust in Web-

20

based Social Networks.”

21

5.

I have been teaching at universities on issues related to computer science, the web,

22

and social media since 1999 when I was a Lecturer in the Computer Science Department at the

23

University of Chicago. Over the past fourteen years, in a variety of different capacities, I have

24

taught classes at University of Chicago, George Mason University, Johns Hopkins University,

25

Georgetown University, George Washington University, American University, and University of

26

Maryland.

27
28

6.

Through my research and studies, I have won a variety of awards including the

2015 University of Maryland Research Communicator Impact Award, 2015 University of
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REPORT OF DR. GOLBECK IN SUPPORT OF
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Maryland System Mentoring Award, Best Paper Award at the 2011 IEEE Social Computing

2

Conference, Best Paper Award at the 2009 International Semantic Web Conference, Research

3

Fellow for the Web Science Research Initiative (2008 – present), IEEE Intelligent Systems Ten to

4

Watch in May 2006, and the 2005 DARPA IPTO Young Investigator Award.

5

7.

I also presented a TED talk titled “The curly fry conundrum: Why social media

6

‘likes’ say more than you might think.”1 It received 1.7 million views and was named one of

7

TED’s “Most Powerful Talks of 2014.”2 TED (Technology, Engineering, Design) is a non-profit

8

organization that presents “Ideas Worth Spreading.” Videos of their invited presentations have

9

over half a billion total views.

10

8.

I have authored over 100 scientific papers related to the web. Most recently, I have

11

authored two books on social media, entitled “Social Media Investigation” and “Analyzing the

12

Social Web.” Both books focus on various aspects of web and social media interaction, such as

13

using location-based services on mobile devices as well as interaction with friends for business

14

purposes. I have also authored other books including “Trust on the World Wide Web: A Survey”

15

and “Art Theory for Web Design.”

16

9.

I have given expert testimony in the following proceedings:
•

Rembrandt Social Media LP v. Facebook Inc. et al, No. 13-cv-00158
U.S. District Court for the Eastern District of Virginia
2013-2014 (plaintiff)

•

Peter Daou and James Boyce vs. Ariana Huffington, Kenneth Lerer and
Thehuffingtonpost.com, No. 651997/2010
Supreme Court of The State Of New York, County of New York 20132014 (plaintiff)

•

17

Blue Calypso Inc. v. Groupon Inc., No. 12-cv-00486,
U.S. District Court for the Eastern District of Texas
2014-2015 (plaintiff)

18
19
20
21
22
23
24
25
26
27
28

1

Available at
https://www.ted.com/talks/jennifer_golbeck_the_curly_fry_conundrum_why_social_media_likes
_say_more_than_you_might_think?language=en.
2
See http://yearinideas.ted.com/2014/.
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REPORT OF DR. GOLBECK IN SUPPORT OF
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10.

A copy of my full curriculum vitae is attached as Exhibit A to this report. I am

2

being compensated at the rate of $400 per hour for my services in this matter, and payment is not

3

contingent on the outcome of this proceeding.

4

II.

5

METHODOLOGY AND SUMMARY OF CONCLUSIONS
11.

I am submitting this report on behalf of the Plaintiffs. I have been retained as a

6

technical expert to study and provide my opinions regarding the topics discussed in paragraph 16

7

below. My opinions, as well as the evidence I rely upon to support them, are set forth in detail in

8

this report. The contents of the various exhibits that I identify by name are meant to be

9

incorporated, in their entirety, by such reference.

10

12.

In preparing this report, I have employed methods and analyses of a type

11

reasonably relied upon by experts in my field in forming opinions or inferences on the subject.

12

The opinions expressed are based upon a reasonable degree of computer science certainty.

13

13.

Between now and such time that I may be asked to testify before the Court, I

14

expect to continue my review, evaluation, and analysis of information generated during

15

discovery, as well as of relevant evidence presented before and/or at trial. I also expect to review

16

the reports submitted by Facebook’s experts. I reserve the right to amend or supplement this

17

report, as necessary and as acceptable to the Court. I also reserve the right to develop materials

18

and exhibits as appropriate for use in helping to demonstrate and explain my opinions in the event

19

that I am asked to testify at trial.

20

14.

In forming my opinions, I have reviewed source code which I understand was

21

provided by Facebook’s counsel and which was represented as containing the relevant source

22

code between some time in 2009 and December 2012.

23

15.

Additionally I have reviewed numerous internal Facebook documents produced in

24

this litigation, as well as certain public materials. The list of documents I have considered in

25

forming my opinions is attached to this report as Exhibit B.

26
27
28

16.

I have been asked by the Plaintiffs through their counsel to opine on the following

issues:
a.

The structure and function of Facebook’s messaging system;
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b.

2

Facebook’s interception of Private Message content, including:
i.

Whether and what devices Facebook employs to intercept message

4

ii.

Whether the interceptions occurred in transit;

5

iii.

Whether the interception of Private Message content was necessary

3

6

content;

for Facebook to deliver private messages;

7

c.

Facebook’s subsequent use of that Private Message content;

8

d.

Whether the class members can be readily determined based on Facebook’s

9

own records; and

10
11
12
13
14
15
16

e.

Whether the Facebook’s uniformly processed Private Messages during the

relevant period.
17.

Based on my review and analysis of Facebook’s source code as well as internal

Facebook documents and deposition testimony, I conclude the following:
a.

The structure and function of Facebook’s messaging system is described in

detail in Section III below;
b.

Facebook intercepted and redirected user’s Private Message content using

17

various code-based devices while the message was in transit, and this interception was not

18

necessary for Facebook to deliver private messages;

19

c.

Facebook used the intercepted Private Message content

20
21
22
23
24

as well as to increment the “Like” social plugin counter;
d.

The class members can be determined from Facebook’s own records using

various query methods and through self-identification; and
e.

Facebook’s source code operated consistently during the relevant period.

25
26
27
28
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REPORT OF DR. GOLBECK IN SUPPORT OF
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1

III.

2

FACEBOOK’S INTERCEPTION OF PRIVATE MESSAGE CONTENT
A.

3

Facebook’s Private Message Architecture Functionality
1.

18.

4

Overview of Facebook’s Private Message Architecture

An overview of Facebook’s Private Message architecture is useful. Say Alice is

5

sending a Private Message to Bob. In order to do so, Alice opens her Facebook message window

6

and begins to compose her message.
Figure 1
Message Window

7
8
9
10
11
12
13
14
15
16
17
18
19
20

19.

She types her text and, if she types, pastes, or otherwise enters a URL into the

body of the message,

21

3

22
23

20.

Facebook describes this process in its Supplemental Responses and Objections to

Plaintiffs’ First Set of Interrogatories:

24
25
26
27
28

3

Facebook’s servers are the computers on which the Facebook system operates. They store code
and data, run the code, provide web content, and manage back-end functionality. Essentially,
every part of Facebook other than the code that runs in the user’s browser is running on Facebook
servers, and those servers provide every element of Facebook that a user interacts with.
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4

2
3

21.

The vast majority of web users have JavaScript enabled. In 2010, Yahoo!

4

engineers issued a report stating that 1-2% of traffic came from users without Javascript enabled.5

5

Those numbers appear to have remained relatively stable over time. A 2013 analysis showed

6

about 1% of users were not accessing JavaScript-based content.6 Thus, 98-99% of users have

7

JavaScript active and this Facebook code would run in their browsers.

8
9

22.

The URL detection process is also described by Ray He, an engineer at Facebook.

In his September 25, 2015 deposition (He Depo.), Mr. He states:

10
11
12
13
23.

Based on my analysis of Facebook’s source code (the “Code”), this process

14
appears in
15
16
17
18
19
20
21
22
23
24
25
26
27
28

4

Id. at 12:26 – 13:2.
See
https://web.archive.org/web/20101016010319/http://developer.yahoo.com/blogs/ydn/posts/2010/
10/how-many-users-have-javascript-disabled/
6
See https://gds.blog.gov.uk/2013/10/21/how-many-people-are-missing-out-on-javascriptenhancement/
7
He Depo. at 187:7-11.
8
FB000027054; FB000027055.
5

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REPORT OF DR. GOLBECK IN SUPPORT OF
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24.

After this Code

2
3
4
9

5
6

25.

The process of

7
10

8
9

26.

As mentioned above,

10
11
12
13
14
15
12

16
17

27.

The preview then appears as part of the message that the Alice is composing

18
13

19
20
21
22
23
24
25
26
27
28

9

Facebook’s Supplemental Responses and Objections to Plaintiffs’ First Set of Interrogatories at
13:4-5.
10
The
is analogous to the “web crawler” referenced in Plaintiffs’ Consolidated
Amended Complaint (“CAC”).
11
API stands for “Application Program Interface.” It's a set of code one can use to interact with a
system (Facebook in this case).
12
FB000027055
13
Facebook’s Supplemental Responses and Objections to Plaintiffs’ First Set of Interrogatories at
13:19-20
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REPORT OF DR. GOLBECK IN SUPPORT OF
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Figure 2
Message Window

2
3
4
5
6
7
8
9
10
11
12
13
28.

However, this preview in Alice’s message is not the

14
. The preview returned is
15
16

14

17
29.

As described in further detail below,

18
19
prior to Alice’s typing
20
the URL into her Private Message, Facebook’s
21
.
22
30.

When Alice finishes the message and hits send, both the text of her message and

23
the

are sent to Facebook’s servers. In a series of steps, detailed further below,

24
Facebook processes the message

, ultimately delivering the message to Bob. For

25
that high level message sending to happen, there are many sub-processes that have to take place.
26
27
28

14

Facebook’s Supplemental Responses and Objections to Plaintiffs’ First Set of Interrogatories at
14:5.
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REPORT OF DR. GOLBECK IN SUPPORT OF
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1

Michael Adkins, a Facebook engineer, provides a broad overview of this transmission process in

2

his October 28, 2015 deposition (Adkins Depo.):

3
4
5
6
7
8
9
10
11
12
13

31.

14

The focus of this report, which is the interception and acquisition of the
in Private Messages, occurs early in the above-described transmission,

15

.

16

B.

17

Facebook’s Interception and
1.

18

32.

of Private Message Content

Creation of Share Objects

Facebook has large and complex data behind its site. They store this in a data

19

model called TAO (The Associations and Objects).16 As the name suggests, there are two pieces

20

in this model: objects and associations.

21

33.

Objects represent things on Facebook – users, pages, checkins, comments,

22

locations, etc. Associations represent relationships between objects. Those could be friendships

23

between users, a like that connects a user to a page, or a location that is tied to a user check-in.

24

34.

25
26

There are a number of objects that Facebook
. Two of these are

15

objects and

Adkins Depo. at 67:23-69:1. Similarly, Ray He stated in his deposition that

27
28

He Depo. at 204:21-24
See https://www.facebook.com/notes/facebook-engineering/tao-the-power-of-thegraph/10151525983993920
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REPORT OF DR. GOLBECK IN SUPPORT OF
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objects. The

2

, while the

3

.

4
5

35.

As described in the previous section, when Facebook is generating the preview for

a URL in a user’s message window,

6
7

It is
information from this

8

object that is used to
. If no

object exists, Facebook’s

9
10
11
17

12
13

36.

Based on my analysis of Facebook’s Code, this is the process for the

14
15
16
17
18
19
20
21

22
23
24
25
26
27
28

17

Facebook’s Supplemental Responses and Objections to Plaintiffs’ First Set of Interrogatories at
13:8-11
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REPORT OF DR. GOLBECK IN SUPPORT OF
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2
3
4
5
6
7
8
9
10
11

cific

12
13

39.

14

Facebook or not,

When the user sends a Private Message containing a URL, whether it is new to
incremented. In other words,

15
16

goes up by 1. The process for this
is as follows.

17
18

40.

After the user hits “send” but before the message is delivered, the Facebook Code

processes information about the sent message. Specific to this litigation, the Code searches for

19

.

20

These

21

internal documentation, “

According to Facebook’s

21

22

In the case of

23
24
25
26

18
19

FB000014199.
In some cases, this includes a

21

FB000014204.
FB000011543.

the private message as the
, known in this case as
. See
Facebook’s Second Supplemental Responses and Objections to Plaintiffs’ Narrowed Second Set
of Interrogatories at14:5-6:

27
28

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REPORT OF DR. GOLBECK IN SUPPORT OF
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Private Messages,
while composing a private message.22

2
3

41.

The creation of the

in

4

the message transmission process. As discussed in greater detail in Section III.B, infra, the Code

5

in

6
7
8
9

42.

Based on my analysis of Facebook’s Code and documents, in my opinion, and as

discussed further below, the

10
11

constitute the
interception, analysis, and use of the contents of user’s Private Message.

12

2.

13

43.

of Private Message Content

Once Facebook intercepts the URL contents of users’ Private

14
15
16

a.

17

44.

Facebook’s Code, as well as Facebook’s internal documents, indicate that when a

18
19

.

20

45.

The

21
22

With this data, Facebook can

23

in a variety of ways. The URLs people share privately may

24
25
26

46.

For example, on
Private Message content.23

27
28

22

An example of the data represented in

is FB000005528.

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REPORT OF DR. GOLBECK IN SUPPORT OF
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1

The particular Facebook functionality at issue in this document is

2

documents describe as

which Facebook

”24 Ray He further explains that

3
25

4
5

47.

Concerning

6
7
8

This

9

indicates if a message was

10
11

This is further corroborated by a

12
26

13
14

Messages were

15
16

48.

This could only be the case if Private
.

Indeed,

in

a later communication, dated

17
27

18
19

49.

In the Facebook Code base, the

20
28

21
22

50.

Again, the presence of the

23

.

24
25
26
27
28

23

FB000002651.
FB000003118.
25
He Depo. at 227:3-4, 11-12.
26
FB000002651.
27
FB000002843.
28
FB000014183.
24

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REPORT OF DR. GOLBECK IN SUPPORT OF
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51.

2
3

6

is now used to

, Facebook could still use information from the Private Messages in the future
since they

4
5

While the addition of the

.
b.

52.

. In Facebook document

FB000008505, Facebook employees describe the

7
8
9
10

53.

In the same document describing this

54.

When a URL is

C.

Facebook’s Use Of Code-Based Devices To Intercept Private Message
Content

55.

As discussed above, Facebook employs various code-based devices to intercept

11
12
13
14
15
16
17
18

Private Message content. Set forth below are the discrete components of Facebook’s Code that

19

execute the interceptions, each of which

20
21
22
23

• Processes

:

o After a user sends a message, the Code

24
25
26
27
28
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REPORT OF DR. GOLBECK IN SUPPORT OF
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•

1
2

o As described in section III.B.2 above,

3
4
5
6
•

7
8

o As described more detail in in paragraph 84 below,

9
10
11
12
13

.
IV.

FACEBOOK’S USES OF INTERCEPTED PRIVATE MESSAGE DATA
A.

Facebook Used Private Message Content To
And Other Features

56.

With the data Facebook collected by scanning Private Messages, Facebook

14
15
16
17
18

.

19

1.

Facebook Provided

20
57.

As described above, the

21
22
23
24
58.

The Code for

25
this is as follows:
26
27
28
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REPORT OF DR. GOLBECK IN SUPPORT OF
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59.

In this

2
3
4
5
6

60.

7
8
9
10

2.

11

61.

12
13
14
15

. This, along with other evidence, strongly
suggests that Facebook continued to use Private Message content to

16
17

62.

As discussed above,

63.

In deposition, Ray He explained that

18
19
20
21
22
23
24
25
26
27
28

29
30

FB000027029.
FB000027051.
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REPORT OF DR. GOLBECK IN SUPPORT OF
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2
3
4
5

64.

Mr. He provided further testimony confirming that Private Message URL sends

were used in

. When discussing how

6

, Ray He was asked whether he was

7
8
9
10
11

”32

12
3.
13

65.

14

. This includes interactions that took place in private messages.

15

66.

In FB000007286, Facebook describes

16
17
18
19
20
21
22
23
24
25
26
27
28

31
32

He Depo. at 234:14-25.
He Depo. at 229:19-230:6.
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Figure 3

2
3
4
5
6
7
8
9
10
11
12
13

67.

14
15
16
17
18

68.

Facebook document FB000006178, which has the subject

69.

Section 4.2 of Facebook document FB000010688 describes

19
20
21
22
23
24
25
26
27
28
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Figure 4

2
3
4
5
6
7
8

The document goes on to explain that the

9
10

70.

As described above,

71.

This indicates that

72.

Facebook document FB000008722 also supports this. It describes

73.

In the

11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
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REPORT OF DR. GOLBECK IN SUPPORT OF
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74.

Experiments by journalist Ashkan Soltani also suggest that Facebook was

2
3
4
5
Figure 5

6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
4.
23

75.

24
25

The Facebook Activity Plugin allowed third parties to show recent activity in
Facebook that related to their site: 33

26
27
28

33

https://web.archive.org/web/20101205130048/http://developers.facebook.com/
docs/reference/plugins/activity.
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REPORT OF DR. GOLBECK IN SUPPORT OF
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The Activity Feed plugin displays the most interesting recent
activity taking place on your site. Since the content is hosted by
Facebook, the plugin can display personalized content whether or
not the user has logged into your site. The activity feed displays
stories both when users like content on your site and when users
share content from your site back to Facebook. If a user is logged
into Facebook, the plugin will be personalized to highlight content
from their friends. If the user is logged out, the activity feed will
show recommendations from your site, and give the user the option
to log in to Facebook.

2
3
4
5
6

The plugin is filled with activity from the user’s friends. If there
isn’t enough friend activity to fill the plugin, it is backfilled with
recommendations. If you set the recommendations param to true,
the plugin is split in half, showing friends activity in the top half,
and recommendations in the bottom half. If there is not enough
friends activity to fill half of the plugin, it will include more
recommendations.

7
8
9
10
76.

11
12
13
14
15
16
17

77.

18
19
20
21
22
23
24
25
26
27
28

34
35

FB000002843.
Id.
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78.

2
3
4
5

5.
79.

Private Message data

80.

Facebook document FB000008499 describes the data that can be seen from

6
7
8
9
10
11
12
13
14
15
16
:
17

Figure 6

18
19
20
21
22
23

81.

24
25
26
27
28
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REPORT OF DR. GOLBECK IN SUPPORT OF
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B.

Incrementing Like Counter

2

82.

On October 3, 2012, the Wall Street Journal reported that Facebook was scanning

3

users’ private messages.36 They detected this by observing that the like count on an external page

4

would increase by two every time the page’s URL was sent in a private message.
83.

5

They tested this by creating pages and observing the count on the external like

6

button increase by two every time the link was sent in a private message. The double counting

7

was also visible on the insights page for webmasters, which shows analytics information.

37

84.

8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28

36
37

http://blogs.wsj.com/digits/2012/10/03/how-private-are-your-private-messages/.
https://developers.facebook.com/blog/post/476.
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2
3
4
5
6
7
8
9
10
11
12
13
14
15

85.

After media reports surfaced the double-counting issue and the fact that private

messages were scanned and hidden “likes” were counted as a result, Facebook

16
17
18
19
20
21

86.

In Facebook document FB000002141, Facebook discusses

22
23
24
25
26
27
28

38

FB000027018.
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1
2
3

87.

Facebook then set out to determine

4
5
.40

6
7

88.

Because the magnitude of

8
9
10
11
12
13

which messages are sent or
received.

14

89.

Indeed, the described change to the Code simply

90.

Changes in

15
16
17
18
19

do not impact the way that

Facebook handles private messages in any way. The Code change that

20
21
22
23
24

91.

This is further highlighted in Facebook document FB000006429. That document,

which discusses

25
26
27
28

39
40

FB000002196.
FB000001599.
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1
2
3

92.

Note that in the changes discussed above, Facebook

93.

This is supported in an email from

4
5
6
7
8
9
10
11
12
41

13
14

V.

15

FACEBOOK’S CONDUCT CONTINUES TO THE PRESENT
A.

Facebook Has

94.

In addressing the issue of

16
17
18
This appears redacted in FB000001606. However, there is no evidence
19
that Facebook ever

.

20
95.

Instead, evidence from the Code shows that Facebook

21
(the last date
22
for which we had access to source Code). I analyzed the files
23
24
25
. There were no substantial changes to these files, and they
26
continued to

until the latest date in the Code.

27
28

41

FB000000425.
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1

96.

Further, internal Facebook documents indicate that Facebook

2
3
4

My
understanding is that Facebook produced several

5
6
7
for the message’s URL.43

8
9

97.

With that information available, Facebook could

10
11
12

VI.

CLASS MEMBERS ARE ASCERTAINABLE

13

A.

Class Members Can Be Identified Through

14

98.

Each

99.

In the Code, the

.

15
16
17
18
19
20
21
22
23
44

24
25
26
27
28

42

FB000005827.
FB000005802-R.
44
FB0000027020.
43

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1

100.

The creator of these private

2
3

as shown below in FB000008499:
Figure 7

4
5
6
7
8
9
10
11

101.

This

12

. That resolves to

13
14
15
16
17
18

102.

person Facebook users located within the United States who have sent, or received from a
Facebook user, private messages that included URLs in their content (and from which Facebook
generated a URL attachment), from within two years of the filing of this action up through the
date of class certification.”

19
20

I understand that the Plaintiffs in this case seek to certify a class of “All natural-

103.

To retrieve a list of class members, the Code process should be relatively

straightforward. A database query could be used

21
22
23

104.

The exact code will vary based on the type of database, but example query code

could roughly take this form:

24
25
26
27

Where

28

correspond to the above examples from the Code.
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1
2

105.

If database queries were not an option, direct code could be written to access the

data. For each

something like the following checks would determine if it were a

3

If so, the

Facebook user ID could be selected:

4
5
6
7
8
9
10

106.

Users could also self-identify as class members. In anyone’s message inbox on

11

Facebook, they can go back and see their old messages. These will indicate if a URL

12

is present because it will have the URL and

13
14
15

in the message.
VII.

THE CODE FOR ANALYZING PRIVATE MESSAGES OPERATED THE SAME
FOR ALL USERS
107.

As described above, I analyzed changes in the relevant portions of the Code over

16
the time period in question. The Code for analyzing private message

operated in the same

17
way for all users. If any Facebook user with a

types in a URL to a

18
private message, Facebook will

. If the user then sends the message,

19
Facebook’s Code would

as described above.

20
21

VIII. “ORDINARY COURSE OF BUSINESS”
108.

I understand that Facebook asserts that the above-described processes are

22
employed in the “ordinary course of business.” I further understand that in the context of the
23
claims that Plaintiffs assert, an electronic communications service provider such as Facebook
24
“cannot simply adopt any revenue-generating practice and deem it “ordinary” by its own
25
subjective standard.” Campbell v. Facebook Inc., 77 F. Supp. 3d 836, 844 (N.D. Cal. 2014).
26
Instead, for an interception to fall within the scope of the defense there must be “some nexus
27
between the need to engage in the alleged interception and the subscriber’s ultimate business, that
28
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1

is, the ability to provide the underlying service or good.” Id. (citing In re Google Inc. Gmail

2

Litig., 2013 U.S. Dist. LEXIS 172784 (N.D. Cal. Sept. 26, 2013).

3

109.

Having reviewed Facebook’s Code and the documents provided in discovery, I

4

conclude that the interception, analysis, and use of URL

5

necessary for the functionality of message sharing in Facebook.

6
7

110.

in Private Messages is not

Facebook itself confirms this in its Interrogatory Responses, where it notes that on

some occasions a message with a URL

8
9
10
11
12
13
46

14
15

111.

Facebook goes on to explain that the URL

16
17
18
19
20
21
7

22
23

112.

The Code also shows that these steps are unnecessary. When a message is sent

24

with

25

the content of the message with its URL to the recipient.

26
27
28

are not necessary to deliver
are

45

Facebook's Second Supplemental Response and Objections to Interrogatory No. 8 at 12:3-7
(emphasis added).
46
Id. 16:24-28 (emphasis added).
47
Facebook’s Supplemental Responses and Objections to Plaintiffs’ First Set of Interrogatories at
15:10-15.
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1

not used to deliver the message. The

2

process. Instead, these processes are related to the acquisition of user’s Private Message content

3

for the purposes described above, such as

4

is not part of the message delivery

, and inflating engagement counts on social plugins. None of those uses fall

5

within a “nexus between the need to engage in the alleged interception and the subscriber’s

6

ultimate business, that is, the ability to provide the underlying service or good.” Campbell, 77 F.

7

Supp. 3d at 844.

8

113.

Additionally, testimony by Michael Adkins demonstrates that Facebook’s

9
10
11

private message content described above.
As explained by Michael Adkins, Facebook’s

12
13
14

For example, Mr. Adkins testified that
Facebook’s

15
16
17
18
19
20

114.

Mr. Adkins further notes that

21
22
23
24

of private message content
described above. Mr. Adkins further confirms this by explaining that

25
26
27
28

48
49

Adkins Depo. at 34:17-25.
Id. at 36:15-18.
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1
2
3

115.

Similarly, Mr. Adkins testified that Facebook’s

4
5
6
7

of private

8

message content described above.

9

IX.

10

“IN TRANSMISSION”
116.

I further understand that Facebook takes the position that the challenged practices

11

occurred “in storage,” as opposed to “in transmission,” and that they are therefore outside of the

12

scope of the statutes through which Plaintiffs bring their claims. My understanding is that an

13

interception must occur “contemporaneously with transmission” in order to have occurred under

14

either the Electronic Communications Privacy Act or the California Invasion of Privacy Act. In

15

re Carrier IQ, Inc., Consumer Privacy Litig., 78 F. Supp. 3d 1051, 1076 (N.D. Cal. 2015). As

16

described above, all the redirection,

17

while the Private Message is in transmission –

18

of Private Message content happens

.

19

117.

As the excerpt of the Adkins Deposition shows quoted in paragraph 30 above,52

20

the message is delivered when it is stored in the

21

message, and any URL

22
23

system. Until that point, the

; otherwise, it could not be in the
computer at all. In the testimony above, Adkins clearly describes a process by which the message

24
25
26

50

Id. at 87:16-21

27
28

52

Id. at 89:11-90:19.
Id. at 67:23-69:1.
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1

is

2

.53

3

118.

4

senders with URLs,

As described above, all the processing of the message, including
and count incrementing, happens before the message is

5

delivered. Thus, in my opinion, Facebook’s interception and redirection of user’s Private

6

Message content happens while the message is in transit and not while it is in storage.

7
8

Dated: November 13, 2015

9
10

__________________

11

Jennifer Golbeck

12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27

53

Ray He’s testimony also confirms this. See He Depo. at 206:4-6

28
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EXHIBIT A

Jennifer Golbeck
College of Information Studies (The iSchool)
University of Maryland
College Park, MD 20742, USA
E-mail: [e-mail]
Homepage: http://www.cs.umd.edu/∼golbeck/

1
1.A

Personal Information
University Appointments

8/2013–present

Associate Professor,
College of Information Studies (100%),
University of Maryland (College Park, Maryland)

8/2007–present

Assistant Professor,
College of Information Studies (100%),
University of Maryland (College Park, Maryland)

1.B

Education

8/2001–5/2005

University of Maryland (College Park, Maryland)
Doctor of Philosophy in Computer Science

9/1999–6/2001

University of Chicago (Chicago, Illinois)
ScientiæMagister in Computer Science

9/1995–6/1999

University of Chicago (Chicago, Illinois)
Scientiæ Baccalaureus in Computer Science
Artium Baccalaureus in Economics

1

1.C

Academic Employment Background

6/2005–8/2007

Faculty Research Associate
Institute for Advanced Computer Studies
Department of Computer Science
University of Maryland (College Park, Maryland)

8/2006–12/2006

Adjunct Professor
Computer Science Department
American University (Washington, DC)

6/2005–9/2006

Research Director
Joint Institute for Knowledge Discovery (JIKD)
University of Maryland (College Park, Maryland)

8/2001–5/2005

Research Assistant
Department of Computer Science
University of Maryland (College Park, Maryland)

8/2001–5/2005

Adjunct Lecturer
Computer Science Department
George Washington University (Washington, DC)

6/2001–5/2003

Adjunct Lecturer
Computer Science Department
Georgetown University (Washington, DC)

5/2002–8/2002

Adjunct Professor
Advanced Physics Lab
Johns Hopkins University (Laurel, Maryland)

8/2001–12/2001

Adjunct Lecturer
Computer Science Department
George Mason University (Fairfax, Virginia)

6/2000–9/2000

Visiting Graduate
Mathematics and Computer Science Division
Futures Laboratory
Argonne National Laboratory (Argonne, Illinois)

6/1999–6/2001

Lecturer
Computer Science Department
University of Chicago (Chicago, Illinois)

2

2

Research, Scholarly, and Creative Activities
• In all references, my name is in bold.
• Unless otherwise indicated, the first author is the lead author.
• Underlined names indicate students with whom I collaborated—this includes students for
whom I am/was the (co-)advisor and other students where the collaboration was limited to
specific projects.
• For work in which a student took the lead, it is customary for the student to be first author,
followed by faculty who have played an advisory or mentoring role, followed by other individuals who have contributed. In cases where a student is listed as the first author, a wavy
::::
underline indicates colleagues with whom I shared this advisory or mentoring role (to the
:::::::::
extent of my knowledge).
• References marked with α indicate that authors are listed in alphabetical order, or that all
co-authors contributed equally.

2.A
2.A.i

Books
Books Authored

B1. Jennifer Golbeck. January 2015. Introduction to Social Media Investigation: A Hands On
Approach. Syngress.
B2. Jennifer Golbeck. 2013. Analyzing the Social Web. Burlington, MA: Morgan Kaufmann.
B3. Jennifer Golbeck. 2008. Trust on the World Wide Web: A Survey. Hanover, MA: Now
Publishers Inc.
B4. Jennifer Golbeck. 2005. Art Theory for Web Design. Boston, MA: Addison–Wesley.
2.A.ii

Books Edited

B5. Jennifer Golbeck (ed). 2008. Computing with Social Trust, London, UK: Springer.
B6. K. Aberer, K.-S.Choi, N. Noy, D. Allemang, K.-I. Lee, L. Nixon, Jennifer Golbeck, P. Mika,
D. Maynard, R. Mizoguchi, G. Schreiber, P. Cudr´ -Mauroux(Eds.) The Semantic Web – 6th
e
International Semantic Web Conference, 2nd Asian Semantic Web Conference (proceedings),
Lecture Notes in Computer Science, Vol. 4825, November 2007.
2.A.iii

Chapters in Books

BC1. Jennifer Golbeck. 2015. “Who Needs an Untrustworthy Doctor? Maslow’s Hierarchy of
Needs” in The Walking Dead Psychology: Psych of the Living Dead, Travis Langley (ed.).
Sterling.
BC2. Ziegler, Cai-Nicolas, and Jennifer Golbeck. ”Models for Trust Inference in Social Networks.” Propagation Phenomena in Real World Networks. Springer International Publishing,
2015. 53-89.

3

BC3. Jennifer Golbeck, Ugur Kuter. 2008. “The Ripple Effect: Change in Trust and Its Impact
over a Social Network” in Computing with Social Trust. Jennifer Golbeck (ed.). Springer.
BC4. Jennifer Golbeck, Aaron Mannes, James Hendler, 2006. “Semantic Web Technologies for
Terrorist Network Analysis,” in Emergent Information Technologies and Enabling Policies
for Counter Terrorism. Robert Popp and John Yen (eds). Wiley-IEEE Press.
BC5. Jennifer Golbeck and Paul Mutton, 2005. “Spring-embedded Graph Visualizations of
Semantic Metadata and Ontologies,” in Visualizing the Semantic Web, 2nd Ed, Vladimir
Geroimenko, Chaomei Chen (eds.). Springer Verlag.
BC6. Jennifer Golbeck, 2004. “IRC with ChatZilla” in Paul Mutton, IRC Hacks, 2004. O’Reilly
Associates: Cambridge, MA.
BC7. Jennifer Golbeck, 2004. “IRC in Mac OS X” in Paul Mutton, IRC Hacks, 2004. O’Reilly
Associates: Cambridge, MA.
BC8. Jennifer Golbeck, 2004. “Getting Friendly with FOAFBot” in Paul Mutton, IRC Hacks,
2004. O’Reilly Associates: Cambridge, MA.
BC9. Jennifer Golbeck, 2004. “Interrogate Trust Networks with TrustBot” in Paul Mutton,
IRC Hacks, 2004. O’Reilly Associates: Cambridge, MA.
BC10. Jennifer Golbeck, 2004. “Check the Weather” in Paul Mutton, IRC Hacks, 2004. O’Reilly
Associates: Cambridge, MA.
BC11. Jennifer Golbeck, 2004. “Convert Currency” in Paul Mutton, IRC Hacks, 2004. O’Reilly
Associates: Cambridge, MA.
BC12. Jennifer Golbeck, 2004. “Don’t Get Lost in Translation” in Paul Mutton, IRC Hacks,
2004. O’Reilly Associates: Cambridge, MA.
BC13. Jennifer Golbeck, 2004. “IRC: Chatrooms for Hackers” in Rael Dornfest and James Duncan Davidson, OS X Panther Hacks, 2004. O’Reilly Associates: Cambridge, MA.
BC14. Jennifer Golbeck, Amy Alford, Ron Alford, James Hendler, 2004. “Organization and
Structure of Information using Semantic Web Technologies,” in Handbook of Human Factors in Web Design, Robert W. Proctor and Kim-Phuong L. Vu (eds.). Lawrence Erlbaum
Associates, NJ.

2.B

Articles in Refereed Journals1

J1. Jennifer Golbeck. Benford’s Law Applies to Online Social Networks. PLoS ONE. in press

3.534

J2. Irene Eleta and Jennifer Golbeck. Multilingual Use of Twitter: Social Networks at the
Language Frontier. Computers in Human Behavior. December 2014

2.489

J3. Jennifer Golbeck and Derek Hansen. A Method for Computing Political Preference Among
Twitter Followers. Social Networks. 36: 20 pages, 2014.

4.059

1

The right column indicates ISI Impact factors of the journal in the year of publication, or most recently available
impact factor otherwise. Missing values indicate that the impact factor is not available.

4

J4. Rebecca LaPlante, Judith :::::::: Jennifer Golbeck. Subject Matter Categorization of
Klavans,
::::::
Tags Applied to Digital Images from Art Museums Journal of the American Society for
Information Science and Technology. 23 pages, in press.

2.137

J5. Awalin Sopan, Manuel Freire, Meirav Taieb-Maimon, :::::::::::::::::: Jennifer GolCatherine Plaisant,
beck, and ::::::::::::::::: Exploring Data Distributions: Visual Design and Evaluation
Ben Shneiderman.
International Journal of Human-Computer Interaction, 29(2), 27 pages, 2013

0.943

J6. Jennifer Golbeck, Jes Koepfler, Beth Emmerling. An Experimental Study of Social Tagging Behavior and Image Content. Journal of the American Society for Information Science
and Technology, 62(9): 1750–1760, 2011.

2.137

J7. Jennifer Golbeck. The more people I meet, the more I like my dog: A study of petoriented social networks on the Web. First Monday, 16(2): 12 pages. 2011
J8. :::::: Watkins, I., Golbeck, J., & Huang, M. Understanding and changing older adults
Xie, B.,
perceptions and learning of social media. Educational Gerontology, (38)4: 282–296, 2011.

0.550

J9. Ugur Kuter and Jennifer Golbeck.α Using Probabilistic Confidence Models for Trust
Inference in Web-Based Social Networks. ACM Transactions on Internet Technology, 10,
2, Article 8 (June 2010), 23 pages, 2010.

2.080

J10. Jennifer Golbeck, Justin Grimes, Anthony Rogers Twitter Use by the US Congress.
Journal of the American Society for Information Science and Technology, 61(8): 1612–
162, 2010.

2.137

J11. P. T. Jaeger, J. Golbeck, A. Druin, K. R. Fleischmann. The First Workshop on the Future
of iSchool Doctoral Education: Issues, Challenges, and Aspirations. Journal of Education
for Library and Information Science, 51(3):201–208, 2010.
J12. Druin, A. Jaeger, P.T., Jennifer Golbeck., Fleischmann, K. R., Lin, J. Qu, Y., Wang,
P. & Xie, B.. The Maryland Modular Method: An Approach to Doctoral Education in
Information Studies. Journal of Education in Library and Information Science (JELIS),
50(4), 293-301, 2010.
J13. Jennifer Golbeck and Christian Halaschek-Wiener. Trust-Based Revision for Expressive
Web Syndication. Journal of Logic and Computation. 19, 5 (October 2009), 771-790.

0.821

J14. Jennifer Golbeck. Trust and Nuanced Profile Similarity in Online Social Networks. ACM
Transactions on the Web, 3, 4, Article 12, 33 pages, 2009.

2.810

J15. Jennifer Golbeck. 2008. Weaving a Web of Trust. Science 19 September 2008: 1640–
1641.

29.78

J16. James Hendler, Jennifer Golbeck. Metcalfe’s Law Applies to Web 2.0 and the Semantic
Web. Journal of Web Semantics. 6(1): 14–20, 2008.

3.410

J17. Jennifer Golbeck. The Dynamics of Web-based Social Networks: Membership, Relationships, and Change. First Monday, 12(11): 1-33, 2007.
J18. Jennifer Golbeck, James Hendler. A Semantic Web and Trust Approach to the Provenance Challenge. Concurrency and Computation: Practice and Experience, 20(5): 431–439,
2007.

5

0.535

J19. L. Moreau, B. Ludascher, I. Altintas, R. S. Barga, S. Bowers, S. Callahan, G. Chin Jr.,
B. Clifford, S. Cohen, S. Cohen-Boulakia, S. Davidson, E. Deelman, L. Digiampietri, I.
Foster, J. Freire, J. Frew, J. Futrelle, T. Gibson, Y. Gil, C. Goble, J. Golbeck, P. Groth,
D. A. Holland, S. Jiang, J. Kim, D. Koop, A. Krenek, T. McPhillips, G. Mehta, S. Miles,
D. Metzger, S. Munroe, J. Myers, B. Plale, N. Podhorszki, V. Ratnakar, E. Santos, C.
Scheidegger, K. Schuchardt, M. Seltzer, Y. L. Simmhan, C. Silva, P. Slaughter, E. Stephan,
R. Stevens, D. Turi, H. Vo, M. Wilde, J. Zhao, and Y. Zhao. The First Provenance
Challenge. Concurrency and Computation: Practice and Experience, 20(5): 409–418, 2007.

0.535

J20. Cai-Nicolas Ziegler, Jennifer Golbeck. Investigating Interactions of Trust and Interest
Similarity. Decision Support Systems, 43(2): 460–475, 2006.

1.190

J21. Richard J. Williams, Neo Martinez, Jennifer Golbeck. Ontologies for Ecoinformatics,
Journal of Web Semantics, 4(4): 237–242, 2006.

3.410

J22. Jennifer Golbeck, James Hendler. Inferring Trust Relationships in Web-Based Social
Networks, ACM Transactions on Internet Technology, 6(4): 497–529, 2006.

0.893

J23. Jennifer Golbeck, Bijan Parsia. Trust network-based filtering of aggregated claims. International Journal of Metadata, Semantics, and Ontologies, 1(1); 58–65, 2005.
J24. Jennifer Golbeck. Semantic Social Networks for Email Filtering: A Prototype and Analysis, AIS SIGSEMIS Bulletin, Vol. 2, Issue (3&4) 2005: 36–40, 2005.
J25. Frank W Hartel, Sherri de Coronado, Robert Dionne, Gilberto Fragoso, Jennifer Golbeck.
Modeling a Description Logic Vocabulary for Cancer Research. Journal of Biomedical
Informatics, 38(2): 114–129, 2005.

1.792

J26. P. Domingos, J. Golbeck, P. Mika, A. Nowak. Trends & Controversies: Social Networks
and Intelligent Systems. IEEE Intelligent Systems, 20(1): 80 – 93, 2005.

1.438

J27. Staab, Steffen, Pedro Domingos, P. Mika, Jennifer Golbeck, Li Ding, Tim Finin, Anupam
Joshi, Andrzej Nowak, and Robin R. Vallacher. Social networks applied. IEEE Intelligent
Systems, page 80-93, 2005.
J28. Aditya Kalyanpur, Jennifer Golbeck, Jay Banerjee, James Hendler. OWL: Capturing
semantic information using a standardized web ontology language. Multilingual Computing
& Technology, 15(7): 8 pages, 2004.
J29. Leslie E. Chipman, Benjamin B. Bederson, Jennifer Golbeck. SlideBar: Analysis of a
linear input device. Behaviour and Information Technology, 23(1): 1–9, 2004.

1.028

J30. Jennifer Golbeck, Gilberto Fragoso, Frank Hartel, Jim Hendler, Jim Oberthaler, Bijan
Parsia. The National Cancer Institute’s Thesaurus and Ontology. Journal of Web Semantics, 1(1): 75–80, 2004.

3.410

2.C

Monographs, Reports, and Extension Publications

R1. Aditya Kalyanpur, James Hendler, Bijan Parsia, Jennifer Golbeck.
markup, ontology, and RDF editor. 2006.

SMORE-semantic

R2. Jennifer Golbeck. Computing and Applying Trust in Web-based Social Networks, Ph.D.
Thesis, University of Maryland, College Park, 2005.
R3. Jennifer Golbeck. Genetic Algorithms for Strategic Optimization. Master’s Thesis, University of Chicago, 2001.
6

2.D

Book Reviews, Other Articles, and Notes

1. “Data Meets Design: a Review of Judith Donath’s The Social Machine”
Science, January 15, 2015
2. “The Live-Tweeted Prostitution Sting Was a Total Bust, and Not in a Good Way”
Slate, May 7, 2014
3. “What a Toilet Hoax Can Tell Us About the Future of Surveillance, on The Atlantic”
The Atlantic, April 29, 2014
4. “Google Tweaked How It Displays Search Results. Heres How to Change It Back”
Slate, March 14, 2014 Slate, January 1, 2014
5. “Beacon, ShopKick: Privacy Policies for location-tracking apps arent clear enough”
Slate January 28, 2014
6. “Facebook Cleansing: How to delete all of your account activity”
Slate, January 1, 2014
7. “Facebook self-censorship: What happens to the posts you don’t publish”
Slate, December 13, 2013
8. “Lovely Spam! Wonderful Spam! (book review of Spam A Shadow History of the Internet)”
Science: Vol. 340 no. 6137 p. 1171, 7 June 2013

2.E
2.E.i

Talks, Abstracts, and Other Professional Papers Presented
Invited Talks: Keynote (and Similar) Addresses

T1. “Privacy, Social Context, and Social Media”
TEDxUMD
College Park, MD (May 3, 2014)
T2. “Trust and Social Media”
AAAI 2013 Fall Symposium Series (Keynote)
Arlington, VA (November 15-17, 2013)
T3. “Hidden Information Uncovered”
TEDxMidAtlantic
Washington, DC (October 25, 2013)
T4. “User Profiling: a two-sided argument”
Conference on Social Computing and Its Applications (Keynote)
Karlsruhe, Germany (October 2, 2013)
T5. “Analyzing the Social Web”
Baltimore Data Day, Federal Reserve Bank of Richmond (Keynote)
Baltimore, MD (July 11, 2013)
T6. “Uncovering Hidden Social Information”
Data Science DC
Washington, DC (March 28, 2013)
7

T7. “Pets on the Internet”
TEDxGeorgetown
Washington, DC (March 23, 2011)
T8. “Tutorial on Using Social Trust for Recommender Systems”
ACM Conference on Recommender Systems (RecSys ’09)
New York, New York (October 22, 2009)
T9. “Computing with Social Trust: Web Algorithms, Social Networks, and Recommendations”
Haverford College Distinguished Visitors Program, and Fantastic Lectures in Computer Science
Series
Haverford, Pennsylvania (March 17, 2009)
T10. “Social Recommender Systems”
SONIC and NICO Lecture Series, Northwestern University
Evanston, Illinois (November 12, 2008)
T11. “The Dynamics of Web-based Social Networks: Membership, Relationships, and Change”
International Sunbelt Social Networking Conference (Sunbelt XXVIII)
St. Pete, Florida (January 22, 2008)
T12. “Social Networks, the Semantic Web, and the Future of Online Scientific Collaboration”
FermiLab Colloquium Lecture
Batavia, Illinois (October 25 2006)
T13. “Trust and Web Policy Systems”
Keynote talk at the Second International Workshop on the Value of Security through Collaboration
Baltimore, Maryland (September 1, 2006)
2.E.ii
2.E.ii.1

Refereed conference proceedings
Papers at Top-Tier Conferences

1

C1. Kan-Leung Cheng and I Zuckerman and D Nau, and J Golbeck ”Predicting Agents Behavior by Measuring their Social Preferences.” Proceedings on the European Conference
on Artificial Intelligence. (2014).
C2. Tammar Shrot, Avi Rosenfeld, Jennifer Golbeck, Sarit Kraus. Timing Interruptions to
Improve User Performance. In Proceedings of the ACM Conference on Human Factors in
Computing Systems (CHI’14). 10 pages. April 2014, Toronto, Canada

23%

C3. Jennifer Golbeck, Eric Norris. Personality, Movie Preferences, and Recommendations. In
Proceedings of the International Conference on Advances in Social Network Analysis and
Mining, 4 pages. August 2013, Niagra Falls, Canada.

15%

C4. Bert Huang, Angelika Kimmig and Lise Getoor and Jennifer Golbeck. Flexible Framework for Probabilistic Models of Social Trust. In 2013 Conference on Social Computing,
Behavioral Modeling and Prediction, 9 pages. April 2013, College Park, MD

31%

1

Conferences with highly-selective acceptance rates and/or top reputations in their field.

8

C5. Carman Neustaedter and Jennifer Golbeck. Exploring pet video chat: the remote awareness and interaction needs of families with dogs and cats. In Proceedings of Computer
Supported Cooperative Work (CSCW’13), in press, 6 pages. February 2013, San Antonio,
TX
C6. Jennifer Golbeck. The Twitter Mute Button: A Web Filtering Challenge. In Proceedings
of the 30th International Conference on Human Factors in Computing Systems (CHI ’12),
pages 2755–2758. May 2012, Austin, TX.

23%

C7. Irene Eleta and Jennifer Golbeck. A Study of Multilingual Social Tagging of Art Images:
Cultural Bridges and Diversity. In Proceedings of Computer Supported Cooperative Work
(CSCW’12), pages 695–704. February 2012, Seattle, Washington

40%

D.,
C8. Cheng, K.L., Zuckerman, I., Nau,::: and Golbeck, J. The Life Game: Cognitive Strate::::
gies for Repeated Stochastic Games. In IEEE Third International Conference on and 2011
IEEE Third International Conference on Social Computing (SocialCom), pages 495-102.
October 2011, Boston, Massachusetts.

10%

C9. Nicholas Violi, Jennifer Golbeck, Kan-leung Cheng, and Ugur Kuter. Caretaker: A
Social Game for Studying Trust Dynamics. In IEEE Third International Conference on
and 2011 IEEE Third International Conference on Social Computing (SocialCom), pages
451–456. October 2011, Boston, Massachusetts.

10%

C10. J. Golbeck, C. Robles, M. Edmondson, and K. Turner. Predicting personality from twitter. In IEEE Third International Conference on and 2011 IEEE Third International Conference on Social Computing (SocialCom), pages 149–156. October 2011, Boston, Massachusetts.

10%

C11. K.L. Cheng, U. Kuter, and J. Golbeck. Coevolving strategies in social-elimination games.
In IEEE Third International Conference on and 2011 IEEE Third International Conference
on Social Computing (SocialCom), pages 118–126. October 2011, Boston, Massachusetts.

10%

C12. Thomas Dubois, Jennifer Golbeck, and ::::::::::::::::::: Network Clustering ApAravind Srinivasan.
proximation Algorithm Using One Pass Black Box Sampling. In Third IEEE International
Conference on Social Computing (SocialCom), pages 418–424. October 2011, Boston, Massachusetts. (Best Paper Award).

10%

Aravind Srinivasan.
C13. Thomas Dubois, Jennifer Golbeck, and ::::::::::::::::::: Predicting Trust and Distrust in Social Networks. In Third IEEE International Conference on Social Computing
(SocialCom), pages 418–424. October 2011, Boston, Massachusetts.

10%

C14. Jennifer Golbeck and Derek Hansen. Computing Political Preference Among Twitter
Followers. In Proceedings of the 29th International Conference on Human Factors in Computing Systems (CHI ’11), pages 1105–1108. April 2011, Vancouver, Canada.

23%

C15. Greg Walsh and Jennifer Golbeck Curator: a game with a purpose for collection recommendation. In Proceedings of the 28th international Conference on Human Factors in
Computing Systems (CHI ’10), pages 2079–2082. April 2010, Atlanta, Georgia.

22%

C16. Freire, M., Plaisant, C., Shneiderman, B., and Golbeck, J. ManyNets: an interface for
multiple network analysis and visualization. In Proceedings of the 28th international Conference on Human Factors in Computing Systems (CHI ’10), pages 213–222. Atlanta,
Georgia, USA, April 10–15, 2010.

22%

9

C17. Ugur Kuter, Jennifer Golbeckα . Semantic Web Service Composition in Social Environments. Proceedings of the International Semantic Web Conference (ISWC09), pages
344–358. November 2009, Washington, D.C. (Best Paper Award)

20%

C18. Thomas DuBois, Jennifer Golbeck, ::::::::::::::::::: Rigorous Probabilistic Trust InAravind Srinivasan.
ference with applications to clustering. Proceedings of the IEEE/WIC/ACM International
Conference on Web Intelligence, pages 655–658. September 2009, Milan Italy.

18%

C19. Derek Hansen, Jennifer Golbeck. Mixing it Up: Recommending Collections of Items.
Proceedings of the Conference on Human Factors in Computing Systems (CHI’09), pages
1217–1226. April 2009, Boston, Massachusetts.

24.5%

C20. Jennifer Golbeck, Matthew Rothstein. Linking Social Networks on the Web with FOAF:
A Semantic Web Case Study. Proceedings of the Twenty-Third National Conference on
Artificial Intelligence (AAAI-08), pages 1138–1143. July 2008, Chicago, Illinois.

24%

C21. Ugur Kuter and Jennifer Golbeckα . SUNNY: A New Algorithm for Trust Inference in
Social Networks, using Probabilistic Confidence Models. Proceedings of the Twenty-Second
National Conference on Artificial Intelligence (AAAI-07), pages 1377–1382. July 2007,
Vancouver, Canada.

27%

C22. Yarden Katz and Jennifer Golbeck. Social Network-based Trust in Prioritized Default
Logic. Proceedings of The Twenty-First National Conference on Artificial Intelligence
(AAAI-06), pages 1345–1350. July 2006, Boston, Massachusetts.

30%

C23. Jennifer Golbeck, James Hendler. Inferring reputation on the semantic web. Proceedings
of the 13th International World Wide Web Conference, 8 pages. May 2004. New York, NY.

14.6%

C24. Jennifer Golbeck, Michael Grove, Bijan Parsia, Aditya Kalyanpur, and James Hendler.
New Tools for the Semantic Web, Proceedings of the 13th International Conference on
Knowledge Engineering and Knowledge Management (EKAW 2002), pages 392–400. October 2002, Siguenza, Spain.

34%

2.E.ii.2

Papers at Other Conferences

C25. Cody Buntain and Jennifer Golbeck. ”Identifying social roles in reddit using network
structure.” Proceedings of the companion publication of the 23rd international conference
on World wide web companion. International World Wide Web Conferences Steering Committee, 2014.
C26. Greg Walsh and Jennifer Golbeck. 2014. StepCity: a preliminary investigation of a
personal informatics-based social game on behavior change. In CHI ’14 Extended Abstracts
on Human Factors in Computing Systems (CHI EA ’14). ACM, New York, NY, USA, 23712376.
C27. Sibel Adali and Jennifer Golbeck. Predicting personality with social behavior. In 2012
IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 8 pages. August 2012, Istanbul, Turkey.
C28. Buntain,Cody, Jennifer Golbeck, Dana::::::::::::::::::::::: Advice and Trust in
Nau, and Sarit Kraus.
:::::
Games of Choice. In Tenth Annual Conference on Privacy, Security and Trust, 2 pages.
July 2012, Paris, France.

10

C29. Jennifer Golbeck, Hal Warren, and Eva Winer. Making trusted attribute assertions
online with the publish trust framework. In Tenth Annual Conference on Privacy, Security
and Trust, 2 pages. July 2012, Paris, France.
C30. David Yates and Jennifer Golbeck. Is facebook appropriate for the classroom? a comparison of student and faculty perspectives. In Proceedings of the Euro-American Conference
for Academic Disciplines and Creativity, 27 pages. June 2012, Prague, Czech Republic.
(Outstanding Research Presentation).
C31. Jennifer Golbeck. STEM initiatives for improved communication skills in the zombie
apocalypse. In Proceedings of the 2012 ACM Conference on Human Factors in Computing
Systems Extended Abstracts, pages 1425–1426. May 2012, Austin, TX.
C32. Jennifer Golbeck and Carman Neustaedter. Pet video chat: monitoring and interacting
with dogs over distance. In Proceedings of the 2012 ACM Conference on Human Factors
in Computing Systems Extended Abstracts, pages 1425–1426. May 2012, Austin, TX.
C33. Jennifer Golbeck, Cristina Robles, Karen Turner Predicting Personality with Social Media. Proceedings of alt.chi, ACM Conference on Human Factors in Computing (CHI 2011),
pages 253–262. April 2011, Vancouver, Canada.
C34. James Michaelis, Jennifer Golbeck, ::::::::::::::: Leveraging the Semantic Web to
James Hendler
Enable Content Mashup For End Users. Proceedings of HCI International 2011, 10 pages.
July 2011, Orlando, Florida.
C35. Jennifer Golbeck, Kenneth Fleischmann. Trust in Social Q &A: The Impact of Text
and Photo Cues of Expertise. Proceedings of ASIST 2010, pages 1–10. October 2010,
Pittsburgh, Pennsylvania.
C36. Klavans, Judith, Jennifer Golbeck. Integrating Multiple Computational Techniques for
Improving Image Access: Applications to Digital Collections. Proceedings of the 2010 Grace
Hopper Conference, 5 pages. September 2010, Atalanta, Georgia.
Jennifer Preece.
C37. Dana Rotman, Jennifer Golbeck, ::::::::::::::: The Community is Where the Rapport
Is: On Sense and Structure in the YouTube Community. 2009 Communities & Technologies
Conference, pages 41–50. June, 2009. University Park, Pennsylvania.
C38. Jennifer Golbeck. On the Internet, Everybody Knows You’re a Dog: The Human-Pet
Relationship in Online Social Networks. ACM Conference on Human Factors in Computing
Systems Extended Abstracts, pages 4495-4500. April 2009, Boston, Massachusetts.
C39. Jennifer Golbeck, Michael Wasser. SocialBrowsing: Integrating Social Networks into Web
Browsing. ACM Conference on Human Factors in Computing Systems Extended Abstracts,
pages 2381–2386. April 2007, San Jose, California.
C40. Aaron Mannes, Jennifer Golbeck. Ontology Building: A Terrorism Specialist’s Perspective. Proceedings of the IEEE Aerospace Conference, 5 pages. March 2007, Big Sky,
Montana.
C41. Aaron Mannes, Jennifer Golbeck. Building a Semantic Web Portal for Counterterror
Analysis. Proceedings of the IEEE Aerospace Conference, 5 pages. March 2007, Big Sky,
Montana.
C42. Jennifer Golbeck, Computing with Trust: Definition, Properties, and Algorithms. Proceedings of International Conference on Security and Privacy in Communication Networks,
pages 1-7. August 2006, Baltimore, Maryland.
11

C43. Jennifer Golbeck. Generating Predictive Movie Recommendations from Trust in Social
Networks. Proceedings of the Fourth International Conference on Trust Management, pages
93–104. May 2006, Pisa, Italy.
C44. Jennifer Golbeck, James Hendler. FilmTrust: Movie recommendations using trust in
web-based social networks. Proceedings of the IEEE Consumer Communications and Networking Conference, pages 497–529. January 2006, Las Vegas, Nevada.
C45. Jennifer Golbeck, Bernardo Cuenca Grau, Christian Halaschek-Wiener, Aditya Kalyanpur, Yarden Katz, Bijan Parsia, Andrew Schain, Evren Sirin, and James Hendler. Semantic
web research trends and directions. Proceedings of the First international Conference on
Pattern Recognition and Machine Intelligence, PReMI. 2005, pages 160-169. December
2005, Kolkata, India.
C46. Jennifer Golbeck, James Hendler. Accuracy of Metrics for Inferring Trust and Reputation in Semantic Web-based Social Networks, Proceedings of 14th International Conference
on Knowledge Engineering and Knowledge Management, pages 116–131. October 2004,
Northamptonshire, UK.
C47. Jennifer Golbeck, James Hendler. Reputation Network Analysis for Email Filtering.
Proceedings of the First Conference on Email and Anti-Spam, pages 54–58. July 2004,
MountableView, California.
C48. Jennifer Golbeck, Bijan Parsia, James Hendler. Trust Networks on the Semantic Web,
Proceedings of Cooperative Information Agents, pages 238–249. August 2003, Helsinki,
Finland.
C49. Mutton, Paul and Jennifer Golbeck. Visualization of Semantic Metadata and Ontologies,
Proceedings of Information Visualization, pages 300–305. July 2003, London, UK.
C50. Kalyanpur, Aditya and Jennifer Golbeck and Michael Grove and Jim Hendler. 2002.
An RDF Editor and Portal for the Semantic Web, Proceedings of Semantic Authoring,
Annotation & Knowledge Markup (ECAI 2002), 4 pages. July 2002, Lyon, France.
C51. Jennifer Golbeck. Evolving Strategies for the Prisoner’s Dilemma, Advances in Intelligent Systems, Fuzzy Systems, and Evolutionary Computation, pages 299–306. February
2002, Interlaken, Switzerland.
2.E.iii.3

Papers at Refereed Workshops

W1. Jennifer Golbeck, Thameem Khan, Nilay Sanghavi and Nishita Thakker. Multiple Personalities on the Web: A Study of Shared Mboxes in FOAF. Proceedings of the 2009 Workshop
on Social Data on the Web, 12 pages. October 2009, Washington, DC.
W2. Thomas DuBois, Jennifer Golbeck, John Kleint, ::::::::::::::::::: Improving RecomAravind Srinivasan.
mendation Accuracy by Clustering Social Neworks with Trust. Proceedings of the ACM RecSys 2009 Workshop on Recommender Systems and the Social Web, 8 pages. October 2009,
New York, New York.
W3. Audun Josang, Jennifer Golbeck, Challenges for robust trust and reputation systems. Proceedings of the 5th International Workshop on Security and Trust Management. 12 pages.
August, 2009, Saint Malo, France.

12

W4. Elena Zheleva, Jennifer Golbeck, Lise ::::::: Ugur Kuter. Using Friendship Ties and
Getoor,
::::
Family Circles for Link Prediction. SNA-KDD Workshop on Social Network Mining and
Analysis, pages 97-113. August 2008, Las Vegas, Nevada.
W5. V. Shiv Naga Prasad, Behjat Siddiquie, Jennifer Golbeck, and :::::::::::::: Classifying
Larry S. Davis.
Computer Generated Charts. In Proceedings of the Workshop on Content Based Multimedia
Indexing, pages 85-92. June 2007, Bordeaux, France.
W6. Jennifer Golbeck, Aaron Mannes. Using Trust and Provenance for Content Filtering on
the Semantic Web. Proceedings of the Workshop on Models of Trust on the Web, 9 pages.
May 2006, Edinburgh, UK.
W7. Christian Halaschek-Wiener, Jennifer Golbeck, Bijan Parsia, Vladimir Kolovski, and ::::
Jim
Hendler. Image browsing and natural language paraphrases of semantic web annotations.
:::::::
First International Workshop on Semantic Web Annotations for Multimedia (SWAMM), 12
pages. May 2006, Edinburgh, UK.
W8. Christian Halaschek-Wiener, Jennifer Golbeck, Andrew Schain, Michael Grove, Bijan Parsia,
and :::::::::::: Annotation and provenance tracking in semantic web photo libraries. ProJim Hendler.
ceedings of the International Provenance and Annotation Workshop, pages 82–89. May 2006,
Chicago, Illinois.
W9. Jennifer Golbeck. Combining Provenance with Trust in Social Networks for Semantic Web
Content Filtering. Proceedings of the International Provenance and Annotation Workshop,
pages 101–108. May 2006, Chicago, Illinois.
W10. Yarden Katz and Jennifer Golbeck. Nonmonotonic Reasoning with Web-Based Social Networks. Proceedings of the Workshop on Reasoning on the Web, pages 469–475. May 2006,
Edinburgh, UK.
W11. Aaron Mannes, Jennifer Golbeck, James Hendler. Semantic Web and Target-Centric Intelligence: Building Flexible Systems that Foster Collaboration. Proceedings of Workshop
Intelligent User Interfaces for Intelligence Analysis, 4 pages. January 2006, Sydney, Australia.
W12. Jennifer Golbeck. Semantic Web Interaction through Trust Network Recommender Systems. End User Semantic Web Interaction Workshop, pages 327–339. November 2005, Sanibel
Island, Florida.
W13. Jennifer Golbeck. Personalizing Applications through Integration of Inferred Trust Values
in Semantic Web-Based Social Networks. Semantic Network Analysis Workshop, pages 1005–
1018. November 2005, Sanibel Island, Florida.
W14. Bijan Parsia, Taowei Wang, and Jennifer Golbeck. Visualizing Web Ontologies with CropCircles. End User Semantic Web Interaction Workshop, pages 1–8. November 2005, Sanibel
Island, Florida.
W15. Christian Halaschek-Wiener, Andrew Schain, Jennifer Golbeck, Michael Grove, Bijan Parsia, Jim Hendler. A flexible approach for managing digital images on the semantic web.
5th International Workshop on Knowledge Markup and Semantic Annotation, pages 49–58.
November 2005, Galway, Ireland.

13

W16. Kalyanpur, Aditya, Nada Hashmi, Jennifer Golbeck, Bijan Parsia. Lifecycle of a Casual
Web Ontology Development Process. Proceedings of the Workshop on Application Design,
Development and Implementation Issues in the Semantic Web, 8 pages. May 2004, New
York, New York.
W17. Jennifer Golbeck, Paul Mutton, Semantic Web Interaction on Internet Relay Chat, Proceedings of Interaction Design on the Semantic Web, 5 pages. May 2004, New York, New
York.
2.E.iii.4

Refereed Posters2

P1. Irena Eleta and Jennifer Golbeck. Bridging Languages in Social Networks: How Multilingual
Users of Twitter Connect Language Communities?, ASIS&T 2012 Annual Meeting, October
2012, Baltimore, Maryland.
P2. Bert Huang and Angelika Kimmigand Lise Getoor and Jennifer Golbeck. Probabilistic Soft
Logic for Trust Analysis in Social Networks, International Workshop on Statistical Relational
AI. August 2012, Catalina Island, CA.
P3. Cristina Robles, Jennifer Golbeck. Facebook Relationships in the Workplace. Proceedings
of CompleNet 2012. March 2012, Marathon, Florida.
P4. Judith:::::::::::: Susan Chun, Jennifer Golbeck, ::::::::::::::::: Robert Stein, Ed
L. Klavans,
Dagobert Soergel,
::::::
Bachta, Rebecca LaPlante, Kate Mayo, John Kleint. Language and Image: T3 = Text, Tags,
and Trust. 2009 Digital Humanities Conference. July 2009, College Park, Maryland.
P5. Jennifer Golbeck, Jeanne Kramer-Smyth. Visualizing Archival Collections with ArchivesZ.
Proceedings of the 2009 Digital Humanities Conference, July 2009, College Park, Maryland.
P6. Praveen Paruchuri, Preetam Maloor, Bob Pokorny, Aaron Mannes, Jennifer Golbeck. Cultural Modeling in a Game-Theoretic Framework, AAAI Fall Symposium on Adaptive Agents
in Cultural Contexts. November 2008, Washington, DC.
P7. Wu, P. F., Qu, Y., Fleischmann, K., Golbeck, J., Jaeger, P., Preece, J., & Shneiderman,
B. Designing a Community-Based Emergency Communication System: Requirements and Implications. Annual Meeting of the American Society for Information Science and Technology
(ASIS&T 2008). October 2008, Columbus, OH.
P8. Jennifer Golbeck, FilmTrust: Movie Recommendations from Semantic Web-based Social
Networks. IEEE Consumer Communications and Networking Conference. January 2006, Las
Vegas, Nevada.
P9. Jennifer Golbeck, FilmTrust: Movie Recommendations from Semantic Web-based Social
Networks. International Semantic Web Conference. November 2005, Galway, Ireland
P10. Halaschek-Wiener, Christian , Jennifer Golbeck, Andrew Schain, Michael Grove, Bijan Parsia,
Jim HendlerPhotostuff-an image annotation tool for the semantic web. Proceedings of the
Poster Track, 4th International Semantic Web Conference. November 2005, Galway, Ireland.
2

Peer-reviewed poster presentations, typically accompanied by short descriptions in associated proceedings.

14

P11. Pin Xu, Lyubov Remennik, N. Rao Thotakura, Jennifer Golbeck, Liju Fan. Prototype
development of an immunology ontology that integrates multiple biomedical ontologies. 7th
International Protege Conference. July 2004, Washington, DC.
P12. Jennifer Golbeck, Bijan Parsia, James Hendler. Trust Networks on the Semantic Web.
Twelfth International World Wide Web Conference, May 2003, Budapest, Hungary.
P13. Jennifer Golbeck, Ron Alford, Ross Baker, Mike Grove, Jim Hendler, Aditya Kalyanpur,
Amy Loomis, Ron Reck. Semantic Web Tools from MINDSWAP. 1st Annual International
Semantic Web Conference, June 2002, Sardinia, Italy.

2.I

Fellowships, Prizes, and Awards
•
•
•
•
•
•
•
•

2.J
2.J.i

2015 University of Maryland Research Communication Award
2014 University System of Maryland Board of Regents Mentoring Award
TED Most Powerful Talks of 2014
2011 IEEE Conference on Social Computing Best Paper Award
2009 International Semantic Web Conference Best Paper Award
Research Fellow, Web Science Research Initiative (2008 – present)
IEEE Intelligent Systems Ten to Watch3 (May 2006)
2005 DARPA IPTO Young Investigator (May 2005)

Editorships, Editorial Boards, and Reviewing Activities for Journals and
Other Learned Publications
Editorial Boards

• Editorial Board, Data Science
• Editorial Committee, Journal of Web Semantics – Special Issue “Exploring New Interaction
Designs Made Possible by the Semantic Web”
• Guest Editor, Security & Privacy Magazine, Special Issue on “Security in Social Networks”
2.J.ii
•
•
•
•
•
•
•
•
•
3

Conference Chair Positions
Program Co-chair, Recsys 2015: Conference on Recommender Systems
Fellowships Chair, ISWC 2012: 11th International Semantic Web Conference
Fellowships Chair, ISWC 2011: 10th International Semantic Web Conference
Tutorials Co-chair, Program Committee Vice Chair, ISWC 2009: 8th International Semantic
Web Conference
Co-organizer, SWUI 2009: Semantic Web User Interactions: Exploring HCI Challenges
Workshop at ISWC09.
Co-organizer, Workshop on Social Technology for Biodiversity: Motivation, Credibility &
Community, 2008
Co-organizer, SWUI 2008: Semantic Web User Interactions: Exploring HCI Challenges
Workshop at CHI’08
Semantic Web Challenge Co-chair, ISWC 2007: 6th International Semantic Web Conference
Semantic Web Challenge Co-chair, ASWC 2007: 2nd Asian Semantic Web Conference

list of top ten young AI researchers

15

•
•
•
•

Co-organizer, Helping Users Make Sense of Social Networks: A Workshop, 2007
Proceedings Chair, ISWC 2006: 5th International Semantic Web Conference
Co-organizer, Workshop on Trust, Security, and Reputation on the Semantic Web, 2006
Organizer, Developers Day Trust on the Web Track, WWW 2005: 13th International World
Wide Web Conference

2.J.iii

Reviewing: Journals

•
•
•
•
•
•
•
•
•
•
•
•
•

ACM Computing Surveys: 2012 (1)
ACM Transactions on Intelligent Systems: 2012 (2)
ACM Transactions on Internet Technology: 2009 (1)
ACM Transactions on Multimedia Computing, Communications, and Applications: 2009 (1)
ACM Transactions on the Web: 2008 (2), 2009(1), 2012 (1)
Behaviour and Information Technology: 2008 (1)
Artificial Intelligence: 2008 (1)
European Journal of Operational Research: 2007 (1)
Foundations and Trends in Information Retrieval: 2015 (1)
Foundations and Trends in Web Science: 2013 (1),
International Journal of Human Computer Studies: 2008 (1)
International Journal on Semantic Web and Information Systems: 2007 (1)
Journal of the American Society for Information Science and Technology: 2010 (1), 2011 (1),
2012 (1)
• Journal of Web Semantics: 2006 (1), 2007 (3), 2008 (1), 2012 (2)
• Policy & Internet: 2012 (1)
2.J.iv

Reviewing: Top-Tier Conferences

• AAAI: AAAI Conference on Artificial Intelligence, Senior Program Committee 2011, 2012;
Program Committee 2006, 2007, 2008, 2009, 2010
• RecSys: ACM Conference on Recommender Systems, Senior Program Committee 2010,
2011, 2012
• CSCW: Computer Supported Cooperative Work, Program Committee 2008, 2012, 2013
• CHI: ACM Conference on Human Factors in Computing, Program Committee 2009, 2010,
2011, 2012
• IJCAI, International Joint Conference on Artificial Intelligence, Program Committee 2009
• WWW: International World Wide Web Conference, Program Committee 2006, 2007, 2008,
2009
• ISWC: International Semantic Web Conference, Senior Program Committee 2009, 2010,
2011, 2012, Program Committee 2008
• KDD: Conference on Knowledge Discovery and Data Mining, Senior Program Committee
2010
• GROUP: Conference on Supporting Group Work, Program Committee 2009
• IJCAI: International Joint Conferences on Artificial Intelligence, Program Committee 2009
2.J.v

Reviewing: Other Venues

• IFIPTM: International Conference on Trust Management, Program Committee 2009, 2010,
2011, 2012
• IEA-AIE: Engineering Knowledge and Semantic Systems, Program Committee 2011
16

• SSW: AAAI Symposium on the Social Semantic Web, Program Committee: 2009
• WebSci: Web Science Conference: Society On-Line International Semantic Web Conference,
Program Committee 2008
• BlogTalk: International Conference on Social Software, Program Committee 2008
• PST: Conference on Privacy, Security and Trust, Program Committee 2008
• SAC: ACM Symposium on Applied Computing, Program Committee 2008, 2005
• CoSoSo: International Conference on Social Software, Program Committee 2008
• IUI: Intelligent User Interfaces Conference, Program Committee 2008
• CEAS: Conference on Email and Anti-Spam, Program Committee 2007
• CIKM: onference on Information and Knowledge Management, Program Committee 2007
• CAT: Context Awareness and Trust, Program Committee 2007
• Policy: IEEE Policy, Program Committee 2007
• SWC: Semantic Web Challenge, Program Committee 2007
• SCCSW: Social and Collaborative Construction of Structured Knowledge Workshop, Program Committee 2007
• SWCKA: AAAI Fall Symposium on Semantic Web for Collaborative Knowledge Acquisition,
Program Committee 2006
• EKAW: International Conference on Knowledge Engineering and Knowledge Management,
Program Committee 2006
• SECOVAL: The Value of Security through Collaboration Workshop, Program Committee
2005, 2006.
• SWUI: Semantic Web User Interaction Workshop, Program Committee 2006
• OWLED: OWL Experiences and Directions, Program Committee 2006
• SPTWS: Workshop on Security, Privacy, and Trust in Web Services, Program Committee
2006
• MTW: Models of Trust Workshop, Program Committee 2006
• OWLED: OWL: Experiences and Directions Workshop, Program Committee 2005
• FOAF: Workshop on Friend of a Friend, Social Networking, and the Semantic Web, Program
Committee 2004
• SWUI: First International Workshop on Interaction Design and the Semantic Web, Program
Committee 2004
• VIKE: Visualizing Information in Knowledge Engineering (VIKE), Program Committee 2003

2.K
2.K.i

Other
External Talks (see section 2.E.i for keynote and similar talks)

• “Opportunities and risks of discovering personality traits from social media”
CHI 2014
Toronto, Canada (May 29, 2014)
• “Predicting User Attributes in Social Media ”
Society 2013
State College, PA (May 9, 2013)
• “Generational Computing and Social Media”
Department of Defense Deep Dive on Obesity
Portsmouth, VA (August 19, 2012)
• “Information Sharing in Social Networks”
FBI Lookout Group Meeting

17

Dallas, TX (August 14, 2012)
• “Social Networks and HCI Research”
National Reconnaissance Office
Chantilly, VA (March 15, 2012)
• “Information Sharing in Social Networks”
Potomac Valley Chapter (PVC) of the American Society of Information Science and Technology
Washington, DC (April 10, 2012)
• “Computing Trust and Personality in Social Networks”
Aberdeen Proving Ground Network Science Meeting
Aberdeen, MD (March 5, 2012)
• “Managing Content With Trust’
Professional & Scholarly Publishers 2012 Annual Conference
Washington, DC (February 2, 2012)
• “Information Sharing in Social Networks”
FBI Lookout Group Meeting
Dallas, TX (January 9, 2012)
• “From Open Data to Open Worlds: The Power of the Semantic Web” World Bank Information
Management Technology Group Forum
Washington, DC (December 8, 2011)
• “Information Sharing in Social Networks”
FBI Headquarters – Counterintelligence Division All-hands Meeting
Washington, DC (November 17, 2011)
• “Predicting Personality from Social Media”
FBI Counterintelligence Behavioral Analysis Unit
Quantico, VA (November 1, 2011)
• “Computing with Social Trust”
Army Research Lab Seminar Series
Adelphi, MD (December 8, 2010)
• “Computing with Social Trust”
Aberdeen Proving Ground CTA Seminar
Aberdeen, MD (November 16, 2010)
• “Personality Traits and Facebook Profiles”
Social and Cognitive Network Academic Research Center Seminar Series
Rensselaer Polytechnic Institute, Troy, NY (April 18, 2010)
• “Social Recommender Systems on the Semantic Web”
National Archives Semantic Web Myth and Fact
Washington, DC (November 17, 2009)
• “Social Software in Digital Libraries and Archives”
Online Computer Library Center (OCLC)
Arlington, VA (November 5, 2009)
• “Recommender Systems, Social Trust, and Television Applications”
StreamSage (a division of Comcast)
Washington, DC (September 9, 2009)
• “Social Networks on the Semantic Web”
Microsoft Research Faculty Summit
Redmond, Washington (July 28, 2008)
• “Understanding Social Networks”
18

•
•
•
•

The 25th Annual Human-Computer Interaction Lab Symposium
College Park, Maryland (May 29, 2008)
“Social Networks and Intelligent Systems: Using Relationships for Information Access”
University of Illinois Urbana-Champaign HCI Seminar
Urbana, Illinois (February 29, 2008)
“Social Networks and the Semantic Web”
Invited talk at Rensselaer Polytechnic Institute
Troy, New York (February 4, 2008)
“Recommending Movies with Social Networks”
Streamsage / Comcast
Washington, DC (November 2007)
“Social Information Access: Connecting Distributed Information and People on the Web”
Presentations with similar titles and content given in the following venues
– Northeastern University College of Computer and Information Science
Boston, Massachusetts (February 2007)
– University of Maryland College of Information Studies
College Park, Maryland (February 2007)
– Drexel College of Information Science and Technology
Philadelphia, Pennsylvania (April 2007)

• “Analysis and Applications of Web-based Social Networks”
University of Illinois at Urbana-Champaign Age of Networks: Social, Cultural, and Technological Connections Speaker Series
Urbana, Illinois (January 22 2007)
• “Provenance Challenge: A Semantic Web Approach”
Global Grid Forum – GGF18/GridWorld
Washington, DC (September 13 2006)
• “The Other Kind of Networking: Social Networks on the Web”
Duke University (March 2006)
• Web-based Social Network Analysis for Socially Intelligent Applications
University of Illinois at Chicago (November 2005)
• “Trust in Social Networks”
National Security Agencys Knowledge Discovery Research Colloquium
Ft. Meade, Maryland (August 2005)
• “Connections, Computation, and Cinema”
Presentations with similar titles and content given in the following venues
– University of Georgia, March 2005.
– MIT Media Lab, March 2005.
• “Inferring Trust in Web-based Social Networks”
National Security Agency
Ft. Meade, Maryland (February 2005)
• “Trust on the Semantic Web”
Thirteenth Annual World Wide Web Conference Developers Day
New York, New York (May 2004)
• “The Semantic Web as a Complex System”
International Conference on Complex Systems
Boston, Massachusetts (May 2004)
19

• “Metadata Visualization Challenges”
NASA Goddard Semantic Web Interest Group
Greenbelt, Maryland (November 2003)
• “Semantic Web: Structure and Modeling”
Half-day workshop at the Howard University
Washington, DC (June 2003)
• “Putting Time into Cognitive Systems: From Real-Time Operating Systems to Information
Dynamics”
Virtual Worlds and Simulation Conference
Orlando, Florida (January 2003)
• “Tools on the Semantic Web”
Half-day workshop at the Howard University
Washington, DC (November2002)
• “Small Worlds on the Semantic Web”
Science on the Semantic Web (SWS) Workshop
Boston, Massachusetts (October 2002)
• “Evolving Strategies for the Prisoners Dilemma”
13th International Conference on Game Theory
Stony Brook, New York (July 2002)
• “Semantic Web Do-It-Yourself: Tools for Generating RDF Content”
NASA Goddard Semantic Web Interest Group
Greenbelt, Maryland (April 2002)

2.K.ii

Internal Talks

• “Video Chat for Pets”
HCIL Symposium
College Park, Maryland (May 22, 2012)
• “Social Network Strategies for Surviving the Zombie Apocalypse”
HCIL Symposium
College Park, Maryland (May 22, 2012)
• “The Twitter Mute Button”
HCIL Symposium
College Park, Maryland (May 22, 2012)
• “Understanding Users and Relationships in Social Networks”
MURI Virtual Brown Bag
College Park, Maryland (April 9, 2012)
• “Computing Trust in Social Networks”
Guest Lecture to PSY228Q: The psychology of social networking and social computing
College Park, Maryland (April 2, 2012)
• “Social Computing 2”
Guest Lecture to CMSC434: Intro to HCI
College Park, Maryland (November 30, 2011)
• “Social Computing 1”
Guest Lecture to CMSC434: Intro to HCI
College Park, Maryland (September 21, 2011)
• “Understanding Users and Relationships in Social Networks”
20

•
•
•
•
•
•
•
•
•

HCIL Symposium
College Park, Maryland (May 25, 2011)
“Trust, Ties, and Information Diffusion in Social Networks”
Guest Lecture in INFM289j: Social Media Campaigns for the WellBeing of Humankind
College Park, Maryland (November 22, 2010)
“Recommender Systems, Social Networks, and Applications”
Guest lecture to CPSP218J: Media, Self, and Society
College Park, Maryland (September 20, 2010)
“Twitter Use by the US Congress”
HCIL Symposium
College Park, Maryland (May 26, 2010)
“Recommender Systems, Social Networks, and Applications”
Guest lecture to CPSP218J: Media, Self, and Society
College Park, Maryland (September 8, 2009)
“Designing Systems to Help Find Experts”
iSchool Colloquium
College Park, Maryland (September 15, 2008)
“Social Networks on the Web: Challenges and Opportunities”
Smith School of Business, University of Maryland
College Park, Maryland (March 14, 2008)
“Social Trust for Information Access”
Center for Information Policy and E-Government (CIPEG) Policy Seminar Series
College Park, Maryland (February 25, 2008)
“Social information access -using social networks to sort, filter, and aggregate”
Human-Computer Interaction Lab (HCIL) Brown Bag Lunch
College Park, Maryland (November 8, 2008)
“Inferring Trust in Social Networks for Information Presentation”
Computational Linguistics and Information Processing (CLIP) Lab Colloquium
College Park, Maryland (October 3, 2007)

2.K.iii

Panels4

• Panelist, Social Media, NewsVision (digital media conference), March 30, 2009, Washington,
DC (invited)
• Panelist, Data Fusion and Data Enrichment Panel, Director of National Intelligence Open
Source Conference, July 2007, Washington, DC (invited)
2.K.iv

Media Mentions

Online and Print Media5
• Huffington Post: The Fall of Facebook - and What’s Next (June 25, 2014)
• Understanding User Generated Tags for Digital Collections: An Interview with Jennifer Golbeck* (May 1, 2013)
• Associated Press: What you ‘like’ on Facebook can be revealing (March 11, 2013)†
• Politico: ‘Weinergate’ a cautionary tale? (May 31, 2011)†
• Daily Caller: Facebook can serve as personality test (May 23, 2011)
4
5

Appearances on panels, not accompanied by papers. Refereed or invited as noted.
†Denotes cases where I was interviewed.

21

• ABC News: Facebook can serve as personality test (May 13, 2011)
• Jezebel: Your Facebook Is The New “Personality Test” (May 13, 2011)
• Time: Put Your Best Face Forward: Facebook Deemed an Accurate Personality Test for
Employers (May 10, 2011)
• ABC Online: Facebook can serve as personality test (May 9, 2011)
• Discovery News: Facebook can serve as personality test (May 9, 2011)
• Seattle Post Intelligencer: What Facebook tells your boss about your personality (May 9,
2011)
• Hindustan Times: Facebooks employee personality test (May 10, 2011)
• New Scientist: Why Facebook friends are worth keeping (July 15, 2010)†
• Corp Comms Magazine: Politicians Tweet Sweet Nothings (September 22, 2009)
• Sacramento Bee: Tweet-tweet goes Schwarzenegger, a big Twitter user (September 22, 2009)†
• Stars & Stripes (U.S. Military Newspaper)
– Japan Edition (September 22, 2009)†
– Mideast Edition (September 22, 2009)†
– Korea Edition (September 21, 2009)†
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•

USTINET News: Study: Congress Tweets Lack Citizen Talk (September 21, 2009)
United Press International: Study: Congress Tweets lack citizen talk (September 21, 2009)†
Baltimore Sun: Congressional Twitter mostly twaddle (September 21, 2009)†
San Diego Union-Tribune: Politicians on Twitter have a lot to say about themselves (September 20, 2009)†
Lawrence Journal World & News: Members of Congress tweet their own horns (September
20, 2009)†
The Telegraph (Calcutta, India): Blowing tweet horns (September 20, 2009)†
The News Journal (Wilmington, DE): For Twitter-happy politicians, the service is all about
them (September 20, 2009)†
Honolulu Advertiser: Politicians Tweets self-promotional (September 20, 2009)†
Huffington Post: Politicians On Twitter: Tweets By Lawmakers Boastful Or Boring: Study
(September 19, 2009)†
Hawaii Reporter: Politicians Tweets Are Mostly Self-Promotional, Researchers Say (September 19, 2009)†
Austin American Statesman: Lawmakers use Twitter for self-promotion, study finds (September 19, 2009)†
The Arizona Republic: Surprise! Twitter from D.C. about self-promotion (September 18,
2009)†
St. Petersburg Times: Times Wires (September 18, 2009)†
The Hill: Lawmakers Tweets Largely Self-Promotional (September 18, 2009)†
The Washington Post: Politicians Tweets Are Mostly Self-Promotional, Researchers Say
(September 18, 2009)†
Politico: Study: Congress Needs Twitter Help (September 16, 2009)†
Kansas City Star: Study: Congress all a Twitter (September 15, 2009)†
Ars Technica: Who do you trust 2.0: Building better preference predictions (September 21,
2008)†
Delmarva Daily Times: ALL ABOUT ME: ‘25 Things’ becomes one of Facebook‘s biggest
fads. (February 27, 2008)†
WEYI NBC25: Facebook backs down on change (February 18, 2009)†
Wired.com: Obama Supporters Act to Clear FUD. (November 12, 2007)†
22

• Physics World: Talking Physics in the Social Web (January 2007)
• Salon.com: You are who you know (June 15, 2004)†
Podcasts, Radio, and TV
• , NPR, The Kojo Nnamdi Show, guest host (January 2014-present)
• NPR, To The Point (January 3, 2014)
• NPR, The Kojo Nnamdi Show, interview on “From :) to GIFS: Expressing ourselves with
images online” (March 21, 2013)
• Wisconsin Public Radio, The Joy Cardin Show, interview on “What your Facebook Likes say
about you” (March 21, 2013)
• NPR, interview on social media and the Olympics (August 1, 2012)
• NPR, The Kojo Nnamdi Show, interview on “The Future Of Neighborhood Communication”
(July 31, 2012)
• NPR, The Kojo Nnamdi Show, interview on “Frictionless Web: Social Readers And Seamless
Sharing” (June 12, 2012)
• NPR, The Kojo Nnamdi Show, interview on “The Interest in Pinterest” (February 28, 2012)
• NPR, The Kojo Nnamdi Show, interview on “The New Sharing Economy” (October 31, 2011)
• NPR, The Kojo Nnamdi Show, interview on “Social Networking Grows Up” (March 24, 2011)
• NPR, The Kojo Nnamdi Show, interview on photos and social media (February 15, 2011)
• NPR, The Animal House, interview on social networks for pets (January 15, 2011)
• BBC World Service Newshour, interview on Twitter use by PMs (October 21, 2009)
• WHIO TV, interview on the use of Twitter by Congress (October 3, 2009)
• KCSN Radio, interview on the use of Twitter by Congress (September 21, 2009)
• WTOP Radio, interview on the use of Twitter by Congress (September 15, 2009)
• NBC 4, TV interview on Facebook data sharing policy (February 17, 2009)
• Science Podcast, interview on trust in social networks (September 18, 2008)
• NBC 4, TV interview on internet predators (February 21, 2008)
• The Diane Rehm Show (National Public Radio), panelist for discussion of Social Networks
(July 10, 2006)

3

Teaching, Mentoring, and Advising

3.A

Courses Taught in the Last Five Years

• INST 775: HCI Capstone Prep
– Fall 2013 (enrollment 12)
• INST 631 / LBSC795: Fundamentals of HCI
– Fall 2012 (enrollment 13)
– Fall 2011 (enrollment 15)
• INST 633 / LBSC708L: Analyzing Social Networks and Social Media
–
–
–
–
–

Spring 2011 (enrollment 22)
Spring 2013 (enrollment 18)
Summer 2013 (enrollment 19)
Winter 2013 (enrollment 13)
Summer 2015 enrollment 20

23

• INFM289I: Social Networks: Technology and Society
– Spring 2010 (enrollment 67)
– Fall 2010 (enrollment 64)
– Spring 2012 (enrollment 75)
• LBSC 690: Information Technology
–
–
–
–
–
–

Summer 2012 (enrollment 18)
Spring 2012 (enrollment 18)
Winter 2011 (enrollment 22)
Winter 2010 (enrollment 16)
Spring 2009 (enrollment 26)
Fall 2007 (enrollment 25)

• LBSC 743: Development of Internet Applications
– Spring 2010 (enrollment 32)
– Fall 2009 (enrollment 27)
– Spring 2009 (enrollment 36)
• LBSC 888: Doctoral Seminar
– Fall 2008 (enrollment 7)
– Spring 2014 (enrollment 7)
• INFM 220: Information Users in Social Context
– Spring 2008 (enrollment 22)
• CMSC 498N: Small Worlds, Social Networks, and Web Algorithms
– Spring 2007 (enrollment 14)

3.B

Course or Curriculum Development

• Fall 2012: Development of HCI Masters capstone classes, INST 775 and 776
• Fall 2011: Redevelopment of LBSC795 / INST631 for HCI Masters program
• Spring 2010: First offering of new course for undergraduate iSeries, INFM289I: Social Networks, Technology, and Society
• Winter 2010. Developed online version of LBSC690: Information Technology
• Spring 2009. First offering of new course. INFM 743: Development of Internet Applications
• Spring 2009. Significant course revision. LBSC 690: Information Technology
• Spring 2008. First offering of new course. INFM 220: Information Users in Social Context
• Fall 2007. Development of new course LBSC 888: Doctoral Seminar (with Allison Druin)
• Spring 2007. First offering of new course. CMSC 498N: Small Worlds, Social Networks, and
Web Algorithms

3.C
3.C.i

Textbooks, Manuals, Notes, Software, Web pages and Other Contributions
to Teaching
Textbooks

• Jennifer Golbeck, Social Network and Social Media Analysis. Burlington, MA: Morgan
Kaufmann, 2013.

24

3.C.ii

Other Contributions to Teaching

• Developed Web-accessible course material (slides, exercises, assignments, sample exams, etc.)
for LBSC 690. Adapted from material by Jimmy Lin and Allison Druin.
• Developed Web-accessible course material for INFM 743.
• Developed Web-accessible course material for INFM 220.
• Built three two-week modules (mini-courses) for LBSC 888.
• Developed and implemented online module system for LBSC 888, where faculty can build
and submit two-week modules for the doctoral seminar.
• Developed Web-accessible course material for CMSC 498N.

3.E
3.E.i

Advising: Other Than Research Direction
Undergraduate

• Anthony Rogers, Individual Studies Program, Fall 2007 – present
• Ben Falk, Individual Studies Program, Spring 2008 – present
• Ryan McCormick, Individual Studies Program, Spring 2008 – present
3.E.ii

Master’s

• Spring 2009: 13 advisees
• Fall 2008: 13 advisees

3.F
3.F.i

Advising: Research Direction
Undergraduate

• Danny Laurence
Spring 2012 – present
Research topic: Computing trust in social networks
• Elaine Wang
Spring 2012 – present
Research topic: Multilingual Use of Twitter
• Vincent Kuyatt (Undergraduate Student, Computer Science)
Spring 2011
Research Topic: Real time strategy games for social strategy analysis
• Michon Edmonson (Undergraduate Student, Computer Science)
Spring 2011 – present
Research Topic: Computing personality and trust
• Wendy Mock (Undergraduate Student, Computer Science)
Spring 2011 – present
Research Topic: Social tagging of images
• Eric Norris (Undergraduate Student, Computer Science)
Summer 2010 – present
Research Topic: processing social network data
25

• Nima Rad (Undergraduate Student, Computer Science)
Summer 2010 – Winter 2011
Research Topic: Games for understanding social strategies
• Karen Turner (Undergraduate Student, Psychology)
Spring 2010 – Fall 2010
Research topic: Use of Facebook
• Anthony Rogers (Undergraduate Student, Individual Studies)
Spring 2008 – present
Research topic: Social networks on the web
• Stuart Moore (Undergraduate Student)
Spring 2008, In the context of INFM 220
Research topic: Expert search
• Joanne Kim (Undergraduate Student)
Spring 2008, In the context of INFM 220
Research topic: Expert search
• Mariya Filippova (Undergraduate Student, Computer Science)
Fall 2007 – Spring 2008
Research topic: Social Applications in Facebook
• Greg Phillips (Undergraduate Student, Computer Science)
Spring 2008, In the context of INFM 220
Research topic: Sentiment analysis in online communities
• Matthew Rothstein (Undergraduate Student, Computer Science)
Spring 2007 – Summer 2008
Research topic: Merging social networks on the Semantic Web with FOAF
• Michael Wasser (Undergraduate Student, Computer Science)
Fall 2005 – Spring 2007
Research topic: Adding social context to web pages
3.F.ii

Master’s

Masters Thesis Committees
• Member, Thesis Committee
Kelly Hoffman (MLS student, the iSchool): Fall 2007 – Spring 2008
• Member, Thesis Committee
Chris Zamerelli (MLS student, the iSchool): Fall 2007 – Spring 2008
• Member, Thesis Committee
D. Adam Anderson (MLS student, the iSchool): Fall 2007 – Spring 2008

26

Other
• Zahra Ashktorab (HCIM Student, the iSchool)
Fall 2011 – present
Research topic; Social Recommender Systems
• Beth Emmerling (PhD student, the iSchool)
Spring 2010 – Fall 2011
Research topic: Image tagging
• Cristina Robles (MLS student, the iSchool)
Spring 2010 – Spring 2011
Research topic: Use of Facebook
• Alon Motro (MIM student, the iSchool)
Spring 2009 – Spring 2012
Research topic: Computing trust in social networks
• Jeanne Kramer-Smyth (MLS student, the iSchool)
Fall 2008 – Spring 2009
In the context of NEH Digital Humanities Startup Grant
Research Topic: Development of ArchivesZ visualization tool for archival collections
• Rishabh Vyas (MIM student, the iSchool)
Fall 2008 – Spring 2009
In the context of a Graduate Research Assistantship (GRA)
Research Topic: expert search through document indexing
• Manasee Mahajan (MIM student, the iSchool)
Fall 2007 – Fall 2008
Research Topic: expert search through document indexing
3.F.iii

Doctoral

As Advisor/Co-Advisor
•
•
•
•
•
•

Advisor, Zahra Ashktorab (Ph.D. Student, iSchool)
Advisor, Cody Buntain (Ph.D. Student, Computer Science)
Advisor, Irene Eleta (Ph.D. Student, iSchool)
Advisor, Jes Koepfler (Ph.D. Student, iSchool)
Advisor, John Kleint (Ph.D. Student, Computer Science)
Co-Advisor with Don Perlis, Hamid Shahri (Ph.D Student, Computer Science)
Graduated Spring 2011
First permanent position: Technology Researcher at the Mayo Clinic
• Co-Advisor with Jim Hendler, Vladimir Kolovski (Ph.D. Student, Computer Science)
Graduated May 2008.
First permanent position: Research Scientist, Oracle (Nashua, NH)
• Co-Advisor with Jim Hendler, Christian Halaschek-Weiner (Ph.D. Student, Computer Science)
Graduated December 2007.
First permanent position: Chief Technology Officer of Clados Management LLC.

27

Other Dissertation Committees
• Member, dissertation committee
Ed Condon (Ph.D. Student, Computer Engineering), Fall 2012–present
• Member, dissertation committee
Jared Sylvester (Ph.D. Student, Computer Science, Fall 2014
• Member, dissertation committee
Megan Monroe (Ph.D. Student, Computer Science), Fall 2013–Spring 2014
• Member, dissertation committee
Kan Leung Cheng (Ph.D. Student, Computer Science), Fall 2010–Summer 2013
• Member, dissertation committee
Greg Walsh (Ph.D. Student, the iSchool), Fall 2010–Summer 2012
• Member, dissertation committee
Bo Han (Ph.D. Student, Computer Science), Summer 2012
• Member, dissertation committee
Tom Dubois (Ph.D. Student, Computer Science), Fall 2010 – Spring 2011
• Member, dissertation committee
Elena Zheleva (Ph.D. Student, Computer Science), Fall 2008 – Spring 2011
• Member, dissertation committee
Hamid Shahri (Ph.D. Student, Computer Science): Fall 2009 – Spring 2011
• Member, dissertation committee
Chuk-Yang Seng (Ph.D. Student, Computer Science): Fall 2008 – Summer 2009
• Member, dissertation committee
Adam Perer (Ph.D. Student, Computer Science): Fall 2007 – Spring 2008
Other6
• Dana Rotman (Ph.D. Student, the iSchool): Fall 2009 – present
Research Topic: Community structure in YouTube
• Tom DuBois (Ph.D. student, Computer Science): Spring 2009 – Spring 2011
Research Topic: Computing trust in social networks
• Justin Grimes (Ph.D. Student, the iSchool): Spring 2009
Research Topic: Twitter Usage in Congress
• Elena Zheleva (Ph.D. Student, Computer Science): Fall 2008 – Spring 2011
Research Topic: Link prediction in social networks
• Christina Pikas (Ph.D. Student, the iSchool): Spring 2008
Research Topic: Social networks in science blogs
• Philip Fei Wu (Ph.D. Student, the iSchool), Fall 2007 – Spring 2008
Research Topic: Community Response Grids
6

Students with whom I have had significant research interaction on specific projects, in a capacity other than
their advisor/co-advisor.

28

4

Service

4.A
4.A.i

Professional
Offices and committee memberships held in professional organizations7

• World Wide Web Consortium Semantic Web Best Practices Working Group, March 2004 –
October 2004
4.A.ii

Reviewing activities for agencies

• Review panelist, NASA Postdoctoral Fellows program Summer 2011
• Review panelist, National Science Foundation (NSF), Directorate for Computer and
mation Science and Engineering (CISE), Spring 2011
• Review panelist, National Science Foundation (NSF), Directorate for Computer and
mation Science and Engineering (CISE), Spring 2010
• Review panelist, National Science Foundation (NSF), Directorate for Computer and
mation Science and Engineering (CISE), Fall 2009
• Review panelist, National Science Foundation (NSF), Directorate for Computer and
mation Science and Engineering (CISE), Fall 2008
• Outside Reviewer, National Science Foundation (NSF), Directorate for Computer and
mation Science and Engineering (CISE), Fall 2007
4.A.iii

InforInforInforInforInfor-

Other unpaid services to local, state, and federal agencies

• Production of video campaign and social media contest for Department of Defense anti-obesity
initiative, in conjunction with Deputy Assistant Secretary of Defense for Health Affairs
Summer 2012 – present

4.B

Campus

4.B.i

College8

•
•
•
•
•
•
•
•
•
•
•
•

Program Director, HCI Masters Program, Summer 2012 – present)
Director, Human-Computer Interaction Lab (Spring 2011 – present)
Member, HCI Masters Committee (Fall 2009 – Spring 2012)
Member, iSchool Search Committee (Fall 2011– Spring 2012)
Chair, iSchool Student Awards Committee (Fall 2011 – Spring 2012)
Co-Director, HCIL (Spring 2009 – Spring 2011)
Chair, iSchool Undergraduate Committee (Fall 2010 – Spring 2011)
Member, iSchool Search Committee (Fall 2010 – Spring 2011)
Member, iSchool Search Committee (Fall 2009 – Spring 2011)
Member, iSchool ad hoc Research Committee (Fall 2009 – Spring 2011)
Member, iSchool Undergraduate Committee (Fall 2008 – Spring 2009)
Assistant Director, Center for Information Policy and E-Government (Fall 2007 – Spring
2010)
• Member, iSchool Doctoral Committee (Fall 2007 – Spring 2010)
7

Position on journal board, chairship/membership on conference program committees, and related reviewing
activities already reported in Section 2.K are not repeated here.
8
Membership on dissertation/examination committees are listed in Section 3.F.iii and not duplicated here.

29

• Secretary, College Assembly (Fall 2008 – Spring 2009)

4.C

University

• Member, University of Maryland Provost Search Committee (Fall 2011 – Spring 2012)

30

EXHIBIT B

Exhibit B: List of Materials Relied On
I relied on the following documents and materials in forming my opinions:
Documents from Campbell, et al. v. Facebook, Inc.:
Supplemental Responses and Objections to Plaintiffs’ First Set of Interrogatories
Facebook’s Second Supplemental Responses and Objections to Plaintiffs’ Narrowed
Second Set of Interrogatories
Deposition of Ray He (September 25, 2015)
Deposition of Michael Adkins (October 28, 2015)
Plaintiffs’ Consolidated Amended Complaint
Exhibit F to the Declaration of Alex Himel on Behalf of Defendant Facebook, Inc.
FB000011543
FB000002651
FB000003118
FB000002651
FB000002843
FB000007286
FB000006178
FB000010688
FB000008505
FB000010688
FB000008722
FB000000298
FB000008643
FB000008499
FB000002141
FB000002190
FB000002196
FB000006429
FB000000699
FB000002197
FB000001599
FB000001608-9
FB000000425
FB000005827
FB000005802-R
FB000008499
Source Code Produced by Facebook

Other Materials
https://web.archive.org/web/20101016010319/http://developer.yahoo.com/blogs/ydn/post
s/2010/10/how-many-users-have-javascript-disabled/
https://gds.blog.gov.uk/2013/10/21/how-many-people-are-missing-out-on-javascriptenhancement/
https://www.facebook.com/notes/facebook-engineering/tao-the-power-of-thegraph/10151525983993920
https://web.archive.org/web/20101205130048/http://developers.facebook.com/
docs/reference/plugins/activity
http://developers.facebook.com/docs/reference/plugins/activity
http://blogs.wsj.com/digits/2012/10/03/how-private-are-your-private-messages/
https://developers.facebook.com/blog/post/476
https://www.facebook.com/1556441609
https://www.facebook.com/michael.s.hurley.73
Campbell v. Facebook Inc., 77 F. Supp. 3d 836, 844 (N.D. Cal. 2014).
In re Google Inc. Gmail Litig., 2013 U.S. Dist. LEXIS 172784 (N.D. Cal. Sept. 26, 2013)
In re Carrier IQ, Inc., Consumer Privacy Litig., 78 F. Supp. 3d 1051, 1076 (N.D. Cal.
2015)

EXHIBIT 3
FILED UNDER SEAL

EXHIBIT 4
FILED UNDER SEAL

EXHIBIT 5
FILED UNDER SEAL

EXHIBIT 6
FILED UNDER SEAL

EXHIBIT 7
FILED UNDER SEAL

EXHIBIT 8
FILED UNDER SEAL

EXHIBIT 9
FILED UNDER SEAL

EXHIBIT 10
FILED UNDER SEAL

EXHIBIT 11
FILED UNDER SEAL

EXHIBIT 12
FILED UNDER SEAL

EXHIBIT 13
FILED UNDER SEAL

EXHIBIT 14
FILED UNDER SEAL

EXHIBIT 15
FILED UNDER SEAL

EXHIBIT 16
FILED UNDER SEAL

EXHIBIT 17
FILED UNDER SEAL

EXHIBIT 18
FILED UNDER SEAL

EXHIBIT 19

Quarterly Earnings Slides
Q4 2012

Safe Harbor
In addition to U.S. GAAP financials, this presentation includes certain non-GAAP financial measures. These
non-GAAP measures are in addition to, not a substitute for or superior to, measures of financial performance
prepared in accordance with U.S. GAAP A reconciliation of non-GAAP financial measures to the corresponding
.
GAAP measures is provided in the appendix to this presentation. Please also see the appendix to this
presentation for information concerning limitations of our key user metrics.

Monthly Active Users (MAUs)
Millions of MAUs
Rest of World
Asia
Europe
US & Canada

680
608
133
138

161
156

739
183
174

800
207

845
225

901
245

955
268

1,007

288

1,056

304

277

298

196

212

234

255

229

239

246

253

261

183

201

212

221

154

163

169

176

179

183

186

189

193

Q4'10

Q1'11

Q2'11

Q3'11

Q4'11

Q1'12

Q2'12

Q3'12

Q4'12

Please see Facebook's Form 10-K for the year ended December 31, 2012 for definitions of user activity used to determine the number of our MAUs, DAUs and
mobile MAUs. The number of MAUs, DAUs, and mobile MAUs do not include Instagram users unless such users would otherwise qualify as MAUs, DAUs, and
mobile MAUs based on activity that is shared back to Facebook.
In June 2012, we discovered an error in the algorithm we used to estimate the geographic location of our users that affected our attribution of certain user
locations for the first quarter of 2012. The first quarter of 2012 user metrics reflect a reclassification to more correctly attribute users by geographic region.
3

Daily Active Users (DAUs)
Millions of DAUs
Rest of World

526

Asia
Europe

417

US & Canada

372
327
58
64

74
72

87
85

457
100

483
109

126

552
139

584
152

119

129

141

618
161

153

98

105

143

152

154

160

169

107

120

127

135

99

105

117

124

126

129

130

132

135

Q4'10

Q1'11

Q2'11

Q3'11

Q4'11

Q1'12

Q2'12

Q3'12

Q4'12

Q4'10

Q1'11

Q2'11

Q3'11

Q4'11

Q1'12

Q2'12

Q3'12

Q4'12

54%

55%

56%

57%

57%

58%

58%

58%

59%

DAUs / MAUs

Please see Facebook's Form 10-K for the year ended December 31, 2012 for definitions of user activity used to determine the number of our MAUs, DAUs and
mobile MAUs. The number of MAUs, DAUs, and mobile MAUs do not include Instagram users unless such users would otherwise qualify as MAUs, DAUs, and
mobile MAUs based on activity that is shared back to Facebook.
For non-worldwide DAU user numbers presented for the periods marked March 31, 2012 and June 30, 2012, the figures represent an average of the first 25 days
of the period and the last 27 days of the period, respectively, due to the algorithm error described in the MAU note on slide 3. These average numbers do not
meaningfully differ from the average numbers when calculated over a full month.
4

Mobile Monthly Active Users (Mobile MAUs)
Millions of Mobile MAUs
680
604
543
488
432

245

Q4'10

288

Q1'11

325

Q2'11

376

Q3'11

Q4'11

Q1'12

Q2'12

Q3'12

Q4'12

Please see Facebook's Form 10-K for the year ended December 31, 2012 for definitions of user activity used to determine the number of our MAUs, DAUs
and mobile MAUs. The number of MAUs, DAUs, and mobile MAUs do not include Instagram users unless such users would otherwise qualify as MAUs,
DAUs, and mobile MAUs based on activity that is shared back to Facebook.

5

Mobile Only Monthly Active Users (Mobile Only MAUs)
Millions of Mobile Only MAUs
157
126
102
83
58

Q4'11

Q1'12

Q2'12

Q3'12

Q4'12

Mobile Only MAUs are mobile MAUs that accessed Facebook solely through mobile apps or our mobile website.

6

Revenue
$5,089

Millions of Dollars

$1,585

Payments and other fees

$810

Advertising

$256

$1,131
$895
$731

$731

$76

$119

$954

$188

$1,058

$3,711

$176

$1,184

$1,262

$557

$192

$186

$156

$94

$1,329
$943

$655

$776

$798

$872

$992

$1,974
$106

$1,086

$637

$4,279
$3,154

$1,868

Q4'10 Q1'11 Q2'11 Q3'11 Q4'11 Q1'12 Q2'12 Q3'12 Q4'12

Quarterly

2010

2011

2012

Annual

7

Revenue by User Geography
Millions of Dollars

$1,585

Rest of World

Europe
US & Canada

$731
$47
$62

$218

$1,131
$895
$65
$82

$954
$78
$104

$1,184

$87

$1,058 $113

$115

$135

$87

$394

$275

$471

$361

$290

$482

$567

$605

$1,262 $198

$3,711
$277

$130
$154

$363

$440

$1,455

$118

$229

$412

$497

$167

Asia

$731
$43
$58

$5,089

$328

$525

$346

$341

$1,974

$1,155

$101
$148

$590

$637

$780

Q4'10 Q1'11 Q2'11 Q3'11 Q4'11 Q1'12 Q2'12 Q3'12 Q4'12

$577

$2,532
$1,914

$1,146

2010

Quarterly

2011

2012

Annual

Revenue by user geography is geographically apportioned based on our estimation of the geographic location of our users when they perform a
revenue-generating activity. This allocation differs from our revenue by geography disclosure in our consolidated financial statements where
revenue is geographically apportioned based on the location of the advertiser or developer.

8

Advertising Revenue by User Geography
Millions of Dollars

$1,329

Rest of World

$156

Asia

$1,086

Europe
US & Canada

$655
$41
$53

$637
$44
$56

$201

$943
$776
$61
$74

$798
$71
$88

$245

$79
$95

$245

$992
$872

$104

$79
$99

$115

$120

$168

$133
$374

$206

$359

$332

Q4'10

Q1'11

$394

$395

Q2'11

Q3'11

$306

$274

$462

$419

Q4'11

Q1'12

$294

$295

$479

$538

Q2'12

Q3'12

$631

Q4'12

Revenue by user geography is geographically apportioned based on our estimation of the geographic location of our users when they perform a
revenue-generating activity. This allocation differs from our revenue by geography disclosure in our consolidated financial statements where
revenue is geographically apportioned based on the location of the advertiser or developer.

9

Payments & Other Revenue by User Geography
Millions of Dollars

$256
$11

Rest of World
Asia

$30

Europe
US & Canada

$76
$2
$5
$17

$94
$3
$6
$23

$53

$62

Q4'10

Q1'11

$119
$4
$8

$156
$7
$16

$188
$8
$20

$186
$8
$19

$192
$9
$20

$55

$54

$52

$176
$10
$21

$66

$46

$45

$30
$149
$77

$87

Q2'11

Q3'11

$105

$106

$111

Q4'11

Q1'12

Q2'12

$99

Q3'12

Q4'12

Revenue by user geography is geographically apportioned based on our estimation of the geographic location of our users when they perform a
revenue-generating activity. This allocation differs from our revenue by geography disclosure in our consolidated financial statements where
revenue is geographically apportioned based on the location of the advertiser or developer.

10

Payments Revenue Recognition Timing
 Payments terms and conditions provide for a 30day claim period following a Payments transaction
during which the customer may dispute the
transaction.
 In Q3 2012 and prior due to insufficient
,
transaction history, Payments revenues were
recognized after the claim period lapsed.
 For example, transactions occurring in June
were recognized as revenue 30 days later in
,
July, and included in Q3 2012 revenue.
Therefore, Q3 2012 revenue reflects
transactions that occurred during the months
of June, July and August.

June

Jul

Aug

Q 3 2012

Sept

Oct

 As of Q4 2012, we had 24 months of historical
transactional information. As a result, starting
in Q4 2012 we recorded all Payments revenues
in the month the transaction occurs, net of
estimated refunds or chargebacks.
This change resulted in a one-time
increase in Payments revenue in
the fourth quarter as we
recognized revenue from an extra
month of payments transactions
(those occurring in September
through December.)

Nov

Q 4 2012

Dec

Jan

Feb

Mar

Q1 2013

11

Average Revenue per User (ARPU)
Worldwide

US & Canada

$1.54

$1.38

$5.02
$1.28

$1.29

$3.20

$3.20

$5.32

$3.40 $4.08
$13.58

$2.90
$11.33

$3.97
$8.34

$1.21

Q4'11 Q1'12 Q2'12 Q3'12 Q4'12

Europe
$1.60

2010

2011

Q4'11 Q1'12 Q2'12 Q3'12 Q4'12

2012

Asia

$1.71

$1.40 $1.43 $1.37

$5.46

2010

2011

2012

Rest of World

$5.91

$2.35

$0.44

$1.84

$0.41
$1.50

$0.37

$2.05

$3.75

$0.47 $0.56

$1.49

$0.94

$0.69
$0.56 $0.53 $0.55 $0.58

Q4'11 Q1'12 Q2'12 Q3'12 Q4'12

2010

2011

2012

Q4'11 Q1'12 Q2'12 Q3'12 Q4'12

2010

2011

2012

Q4'11 Q1'12 Q2'12 Q3'12 Q4'12

2010

2011

2012

Revenue by user geography is geographically apportioned based on our estimation of the geographic location of our users when they perform
a revenue-generating activity. This allocation differs from our revenue by geography disclosure in our consolidated financial statements where
revenue is geographically apportioned based on the location of the advertiser or developer. The ARPU amount for US & Canada region in Q1
2012 reflects an adjustment based on the reclassification of certain users between geographical regions to more correctly attribute users by
geographic region. Please see Facebook's Form 10-K for the year ended December 31, 2012 for definitions of ARPU and annual ARPU.
12

Share-Based Compensation Expense
$1,106

Millions of Dollars
Pre-2011 RSUs
Post-2011 RSUs
Options & Other

$986

$64

$70

$76

$179
$28

$103

$6

$7

$58

$59

$74

$97

Q4'10

Q1'11

Q2'11

Q3'11

Q4'11

Q1'12

$184
$24

$113

$138

$137

Q2'12

Q3'12

Q4'12

Q4 2012 expenses are estimates and exclude any potential impact of future acquisitions.

13

Expenses as a % of Revenue
Share-based compensation + Payroll tax related to share-based compensation

Cost of Revenue

Research & Development
31%

26%

26%

22%

22%

Q4'11

26%

25%

25%

60%

25%

24%

19%

19%

11%
Q1'12

Q2'12

Q3'12

Q4'12

Marketing & Sales

14%

14%

7%
Q4'11

9%

9%

11%

10%

Q1'12

Q2'12

Q3'12

Q4'12

General & Administrative

33%

11%

All other expenses

39%

13%

12%
8%

9%

12%

11%

12%

10%

Q4'11

Q1'12

Q2'12

Q3'12

Q4'12

7%

8%

Q4'11

Q1'12

12%

11%

11%

10%

9%

Q2'12

Q3'12

Q4'12

10%

We have reclassified certain prior period amounts in marketing and sales to general and administrative expense to conform to our current
period presentation. These reclassifications did not affect revenue, total costs and expenses, income (loss) from operations, or net (loss)
income.

14

GAAP Income (Loss) from Operations & Margin
Income (Loss) from Operations ($M)
$437

$388

$407

$414

$548

$381

$377

$523

($743)
Q4'10

Q1'11

Q2'11

Q3'11

Q4'11

45%

43%

48%

Q1'12

Q3'12

Q4'12

30%

Q2'12

33%

Q3'12

Q4'12

Operating Margin
60%

53%

36%

(63%)
Q4'10

Q1'11

Q2'11

Q3'11

Q4'11

Q1'12

Q2'12

15

Effective Tax Rate
Q1
2012

(in millions)
Revenue

$

Q2
2012

1,058

$

1,184

Q3
2012
$

1,262

Q4
2012
$

1,585

2011
$

3,711

2012
$

5,089

Costs and expenses:
Cost of revenue
Research and development
Marketing and sales
General and administrative
Total costs and expenses
Income from operations

277
153
143
104
677
381

Interest and other income (expense), net
Interest expense

(13)

(10)

(11)

(16)

(42)

(51)

Other income (expense), net
Income (loss) before provision for income taxes
Provision for (benefit from) income taxes
Net income (loss)

14
382
177
205

(12)
(765)
(608)
(157)

6
372
431
(59)

(2)
505
441
64

(19)
1,695
695
1,000

7
494
441
53

Effective Tax Rate

$

46%

367
705
392
463
1,927
(743)

$

79%

322
244
168
151
885
377

$

398
297
193
174
1,062
523

$

116%

87%

860
388
393
314
1,955
1,756

$

41%

1,364
1,399
896
892
4,551
538

$

89%

Q2 through Q4 effective tax rates were influenced by significant share-based compensation expense resulting from our initial public offering,
a portion of which is not tax-deductible

16

GAAP Net Income (Loss)
Millions of Dollars
$302
$251

$233

$240

$227

$205

$64

($59)
($157)
Q4'10

Q1'11

Q2'11

Q3'11

Q4'11

Q1'12

Q2'12

Q3'12

Q4'12

17

Non-GAAP Net Income
Millions of Dollars
$1,317
$1,164

$360

$426

Q4 2011
Q4 2012
Quarterly

2011

2012
Annual

Non-GAAP net income excludes share-based compensation expense, payroll tax expenses related to share-based compensation, and related
income tax adjustments—see the Appendix for a reconciliation of this non-GAAP measure to GAAP net income.

18

Capital Investments
Mi llions of Dollars

$1,575

Property and equipment acquired

$340

under capital leases
Purchases of property
and equipment

$1,079

$473
$1,235

$510
$217
$606
$89
$56
$33

$293

2009

2010

2011

2012

Annual

19

Employees
Period-end Headcount

4,619

3,200
2,127
1,218

2009

2010

2011

2012

Annual

20

Appendix

Reconciliations

Three Months Ended
December 31,
GAAP net income
Share-based compensation expense
Payroll tax expenses related to share-based compensation
Income tax adjustments
Non-GAAP net income

2011
$
302
76
(18)
$
360

2012
$

$

64
184
29
149
426

Year Ended
December 31,
2011
$ 1,000
217
7
(60)
$ 1,164

2012
$

$

53
1,572
151
(459)
1,317

22

Limitations of Key Metrics
The numbers of our monthly active users (MAUs) and daily active users (DAUs) and average revenue per user
(ARPU) are calculated using internal company data based on the activity of user accounts. While these numbers
are based on what we believe to be reasonable estimates of our user base for the applicable period of
measurement, there are inherent challenges in measuring usage of our products across large online and mobile
populations around the world. For example, there may be individuals who maintain one or more Facebook
accounts in violation of our terms of service, despite our efforts to detect and suppress such behavior. We
estimate, for example, that “duplicate” accounts (an account that a user maintains in addition to his or her principal
account) may have represented approximately 5.0% of our worldwide MAUs as of December 31, 2012. We also
seek to identify “false” accounts, which we divide into two categories: (1) user-misclassified accounts, where users
have created personal profiles for a business, organization, or non-human entity such as a pet (such entities are
permitted on Facebook using a Page rather than a personal profile under our terms of service); and (2)
undesirable accounts, which represent user profiles that we determine are intended to be used for purposes that
violate our terms of service, such as spamming. As of December 31, 2012, for example, we estimate usermisclassified accounts may have represented approximately 1.3% of our worldwide MAUs and undesirable
accounts may have represented approximately 0.9% of our worldwide MAUs. We believe the percentage of
accounts that are duplicate or false is meaningfully lower in developed markets such as the United States or
Australia and higher in developing markets such as Indonesia and Turkey. However, these estimates are based on
an internal review of a limited sample of accounts and we apply significant judgment in making this determination,
such as identifying names that appear to be fake or other behavior that appears inauthentic to the reviewers. As
such, our estimation of duplicate or false accounts may not accurately represent the actual number of such
accounts. We are continually seeking to improve our ability to identify duplicate or false accounts and estimate the
total number of such accounts, and such estimates may be affected by improvements or changes in our
methodology.

23

Limitations of Key Metrics (continued)
Some of our historical metrics through the second quarter of 2012 have also been affected by applications on
certain mobile devices that automatically contact our servers for regular updates with no user action involved, and
this activity can cause our system to count the user associated with such a device as an active user on the day
such contact occurs. For example, we estimate that less than 5% of our estimated worldwide DAUs as of
December 31, 2011 and 2010 resulted from this type of automatic mobile activity, and that this type of activity had
a substantially smaller effect on our estimate of worldwide MAUs and mobile MAUs. The impact of this automatic
activity on our metrics varies by geography because mobile usage varies in different regions of the world. In
addition, our data regarding the geographic location of our users is estimated based on a number of factors, such
as the user’s IP address and self-disclosed location. These factors may not always accurately reflect the user’s
actual location. For example, a mobile-only user may appear to be accessing Facebook from the location of the
proxy server that the user connects to rather than from the user’s actual location. The methodologies used to
measure user metrics may also be susceptible to algorithm or other technical errors. For example, in early June
2012, we discovered an error in the algorithm we used to estimate the geographic location of our users that
affected our attribution of certain user locations for the period ended March 31, 2012. While this issue did not
affect our overall worldwide MAU number, it did affect our attribution of users to different geographic regions. We
estimate that the number of MAUs as of March 31, 2012 for the United States & Canada region was overstated as
a result of the error by approximately 3% and these overstatements were offset by understatements in other
regions. In addition, our estimates for revenue by user location are also affected by these factors. We regularly
review and may adjust our processes for calculating these metrics to improve their accuracy. In addition, our MAU
and DAU estimates will differ from estimates published by third parties due to differences in methodology. For
example, some third parties are not able to accurately measure mobile users or do not count mobile users for
certain user groups or at all in their analyses.
The number of MAUs, DAUs, mobile MAUs, and ARPU discussed in these slides do not include users of
Instagram unless such users would otherwise quality as MAUs, DAUs, and mobile MAUs, respectively, based on
activity that is shared back to Facebook.

24

EXHIBIT 20

REDACTED VERSION OF DOCUMENT(S) SOUGHT TO BE SEALED
1
2
3
4
5
6
7
8
9
10
11
12
13
14

GIBSON, DUNN & CRUTCHER LLP
JOSHUA A. JESSEN, SBN 222831
[e-mail]
JEANA BISNAR MAUTE, SBN 290573
[e-mail]
ASHLEY M. ROGERS, SBN 286252
[e-mail]
1881 Page Mill Road
Palo Alto, California 94304
Telephone: [phone]
Facsimile: [phone]
GIBSON, DUNN & CRUTCHER LLP
GAIL E. LEES, SBN 90363
[e-mail]
CHRISTOPHER CHORBA, SBN 216692
[e-mail]
333 South Grand Avenue
Los Angeles, California 90071
Telephone: [phone]
Facsimile: [phone]
Attorneys for Defendant
FACEBOOK, INC.

15

UNITED STATES DISTRICT COURT

16

NORTHERN DISTRICT OF CALIFORNIA

17

OAKLAND DIVISION

18
19

MATTHEW CAMPBELL, MICHAEL
HURLEY, and DAVID SHADPOUR,
Plaintiffs,

20
21

v.

22

FACEBOOK, INC.,

23

Defendant.

Case No. C 13-05996 PJH (MEJ)
PUTATIVE CLASS ACTION
DEFENDANT FACEBOOK, INC.’S
SUPPLEMENTAL RESPONSES AND
OBJECTIONS TO PLAINTIFFS’
NARROWED SECOND SET OF
INTERROGATORIES

24
25
26
27
28
Gibson, Dunn &
Crutcher LLP

DEFENDANT FACEBOOK, INC.’S SUPPLEMENTAL RESPONSES AND OBJECTIONS TO
PLAINTIFFS’ NARROWED SECOND SET OF INTERROGATORIES
Case No. C 13-05996 PJH (MEJ)

1

Defendant Facebook, Inc. (“Defendant” or “Facebook”), by and through its attorneys, and

2

pursuant to Rules 26 and 33 of the Federal Rules of Civil Procedure, the Local Civil Rules of the U.S.

3

District Court for the Northern District of California, the Court orders in this action, and the parties’

4

agreements, provides the following supplemental responses and objections to Plaintiffs’ Narrowed

5

Second Set of Interrogatories (the “Interrogatories”).
PRELIMINARY STATEMENT

6
7

1.

Facebook’s responses to the Interrogatories are made to the best of Facebook’s current

8

knowledge, information, and belief. Facebook reserves the right to supplement or amend any of its

9

responses should future investigation indicate that such supplementation or amendment is necessary.

10

2.

Facebook’s responses to the Interrogatories are made solely for the purpose of and in

11

relation to this action. Each response is given subject to all appropriate objections (including, but not

12

limited to, objections concerning privilege, competency, relevancy, materiality, propriety, and

13

admissibility). All objections are reserved and may be interposed at any time.

14
15
16

3.

Facebook’s responses are premised on its understanding that Plaintiffs seek only that

information that is within Facebook’s possession, custody, and control.
4.

Facebook incorporates by reference each and every general objection set forth below

17

into each and every specific response. From time to time, a specific response may repeat a general

18

objection for emphasis or some other reason. The failure to include any general objection in any

19

specific response shall not be interpreted as a waiver of any general objection to that response.

20

5.

Nothing contained in these Reponses and Objections or provided in response to the

21

Interrogatories consists of, or should be construed as, an admission relating to the accuracy,

22

relevance, existence, or nonexistence of any alleged facts or information referenced in any

23

Interrogatory.
GENERAL OBJECTIONS

24
25

1.

Facebook objects to each Interrogatory, including the Definitions and Instructions, to

26

the extent that it purports to impose obligations beyond those imposed by the Federal Rules of Civil

27

Procedure, the Federal Rules of Evidence, the Local Civil Rules of the U.S. District Court for the

28

Northern District of California, and any agreements between the parties.
1

Gibson, Dunn &
Crutcher LLP

DEFENDANT FACEBOOK, INC.’S SUPPLEMENTAL RESPONSES AND OBJECTIONS TO
PLAINTIFFS’ NARROWED SECOND SET OF INTERROGATORIES
Case No. C 13-05996 PJH (MEJ)

1

2.

Facebook objects to each Interrogatory to the extent that it is not limited to the

2

relevant time period, thus making the Interrogatory overly broad, unduly burdensome, and not

3

relevant to the claims or defenses in this action. Unless otherwise specified in its responses, and

4

pursuant to the agreement of the parties, Facebook’s responses will be limited to information

5

generated between April 1, 2010 and December 30, 2013.

6

3.

Facebook objects to each Interrogatory to the extent that it seeks information unrelated

7

and irrelevant to the claims or defenses in this litigation and not reasonably calculated to lead to the

8

discovery of admissible evidence.

9

4.

Facebook objects to each Interrogatory as overly broad and unduly burdensome,

10

particularly in view of Facebook’s disproportionate cost necessary to investigate as weighed against

11

Plaintiffs’ need for the information. The Interrogatories seek broad and vaguely defined categories of

12

materials that are not reasonably tailored to the subject matter of this action.

13

5.

Facebook objects to each Interrogatory to the extent that it purports to request the

14

identification and disclosure of information or documents that were prepared in anticipation of

15

litigation, constitute attorney work product, reveal privileged attorney-client communications, or are

16

otherwise protected from disclosure under any applicable privileges, laws, or rules. Facebook hereby

17

asserts all such applicable privileges and protections, and excludes privileged and protected

18

information from its responses to each Interrogatory. See generally Fed. R. Evid. 502; Cal. Code

19

Evid. § 954. Inadvertent production of any information or documents that are privileged or otherwise

20

immune from discovery shall not constitute a waiver of any privilege or of any other ground for

21

objecting to the discovery with respect to such information or documents or the subject matter

22

thereof, or the right of Facebook to object to the use of any such information or documents or the

23

subject matter thereof during these or any other proceedings. In the event of inadvertent disclosure

24

of any information or inadvertent production or identification of documents or communications that

25

are privileged or otherwise immune from discovery, Plaintiffs will return the information and

26

documents to Facebook and will be precluded from disclosing or relying upon such information or

27

documents in any way.

28
Gibson, Dunn &
Crutcher LLP

6.

Facebook objects to each and every Interrogatory to the extent that the information
2

DEFENDANT FACEBOOK, INC.’S SUPPLEMENTAL RESPONSES AND OBJECTIONS TO
PLAINTIFFS’ NARROWED SECOND SET OF INTERROGATORIES
Case No. C 13-05996 PJH (MEJ)

1

sought by the Interrogatory is more appropriately pursued through another means of discovery, such

2

as a request for production or deposition.

3
4
5

7.

Facebook objects to each and every Interrogatory, Definition, and Instruction to the

extent that it seeks information outside of Facebook’s possession, custody, and control.
8.

Facebook objects to each Interrogatory to the extent that it requests information

6

protected by the right of privacy of Facebook and/or third parties, or information that is confidential,

7

proprietary, or competitively sensitive.

8
9
10
11

9.

Facebook objects to each Interrogatory to the extent that it seeks documents or

information already in Plaintiffs’ possession or available in the public domain. Such information is
equally available to Plaintiffs.
10.

Facebook objects to each Interrogatory on the ground and to the extent that it exceeds

12

the bounds of Federal Rule of Civil Procedure 33(a)(1), which provides that “a party may serve on

13

any other party no more than 25 written interrogatories, including all discrete subparts.”
OBJECTIONS TO DEFINITIONS

14
15

1.

Facebook objects to Plaintiffs’ definition of “Association” to the extent that it is

16

vague, ambiguous, overly broad, and unduly burdensome. Facebook further objects to the definition

17

to the extent that Plaintiffs purport to use this defined term to seek materials that are not relevant to

18

the claims and defenses in this action.

19

2.

Facebook objects to Plaintiffs’ definition of “Association Type” or “(atype)” to the

20

extent that it is vague, ambiguous, overly broad, and unduly burdensome. Facebook further objects

21

to the definition to the extent that Plaintiffs purport to use this defined term to seek materials that are

22

not relevant to the claims and defenses in this action.

23

3.

Facebook generally objects to Plaintiffs’ definitions of “Communication,”

24

“Document(s),” “Electronic Media,” “ESI,” “Electronically Stored Information,” “Identify,” and

25

“Metadata” to the extent that Plaintiffs purport to use these defined terms to request the identification

26

and disclosure of documents that: (a) were prepared in anticipation of litigation; (b) constitute

27

attorney work product; (c) reveal privileged attorney-client communications; or (d) are otherwise

28

protected from disclosure under any applicable privileges, laws, and/or rules. Facebook further
3

Gibson, Dunn &
Crutcher LLP

DEFENDANT FACEBOOK, INC.’S SUPPLEMENTAL RESPONSES AND OBJECTIONS TO
PLAINTIFFS’ NARROWED SECOND SET OF INTERROGATORIES
Case No. C 13-05996 PJH (MEJ)

1

objects to the extent that these definitions purport to impose obligations that go beyond the

2

requirements of the Federal and Local Rules.

3

4.

Facebook objects to Plaintiffs’ definition of “Destination Object” or “(id2)” to the

4

extent that it is vague, ambiguous, overly broad, and unduly burdensome. Facebook further objects

5

to the definition to the extent that Plaintiffs purport to use this defined term to seek materials that are

6

not relevant to the claims and defenses in this action.

7

5.

Facebook objects to Plaintiffs’ definition of “(id)” to the extent that it is vague,

8

ambiguous, overly broad, and unduly burdensome. Facebook further objects to the definition to the

9

extent that Plaintiffs purport to use this defined term to seek materials that are not relevant to the

10
11

claims and defenses in this action.
6.

Facebook objects to Plaintiffs’ definition of “Key -> Value Pair” to the extent that it is

12

vague, ambiguous, overly broad, and unduly burdensome. Facebook further objects to the definition

13

to the extent that Plaintiffs purport to use this defined term to seek materials that are not relevant to

14

the claims and defenses in this action.

15

7.

Facebook objects to Plaintiffs’ definition of “Object” to the extent that it is vague,

16

ambiguous, overly broad, and unduly burdensome. Facebook further objects to the definition to the

17

extent that Plaintiffs purport to use this defined term to seek materials that are not relevant to the

18

claims and defenses in this action.

19

8.

Facebook objects to Plaintiffs’ definition of “Object type” or “(otype)” to the extent

20

that it is vague, ambiguous, overly broad, and unduly burdensome. Facebook further objects to the

21

definition to the extent that Plaintiffs purport to use this defined term to seek materials that are not

22

relevant to the claims and defenses in this action.

23

9.

Facebook objects to Plaintiffs’ definition and use of the term “Person” as vague,

24

ambiguous, overly broad, and unduly burdensome to the extent that Plaintiffs intend to use this term

25

to include “any natural person or any business, legal or governmental entity or association” over

26

which Facebook exercises no control.

27
28
Gibson, Dunn &
Crutcher LLP

10.

Facebook objects to Plaintiffs’ definition of “Process” to the extent that it is vague,

ambiguous, overly broad, and unduly burdensome. Facebook further objects to the definition to the
4
DEFENDANT FACEBOOK, INC.’S SUPPLEMENTAL RESPONSES AND OBJECTIONS TO
PLAINTIFFS’ NARROWED SECOND SET OF INTERROGATORIES
Case No. C 13-05996 PJH (MEJ)

1

extent that Plaintiffs purport to use this defined term to seek materials that are not relevant to the

2

claims and defenses in this action.

3

11.

Facebook objects to Plaintiffs’ definition of “Private Message(s)” to the extent that it

4

is vague, ambiguous, overly broad, and unduly burdensome. Facebook further objects to the

5

definition to the extent that Plaintiffs purport to use this defined term to seek materials that are not

6

relevant to the claims and defenses in this action.

7

12.

Facebook objects to Plaintiffs’ definitions of “Relate(s) to,” “Related to” and

8

“Relating to” on the ground that the definitions make the Interrogatories overly broad and unduly

9

burdensome and impose obligations that go beyond the requirements of the Federal and Local Rules.

10
11

Facebook shall construe these terms as commonly and ordinarily understood.
13.

Facebook objects to Plaintiffs’ definition of “Source Object” or “(id1)” to the extent

12

that it is vague, ambiguous, overly broad, and unduly burdensome. Facebook further objects to the

13

definition to the extent that Plaintiffs purport to use this defined term to seek materials that are not

14

relevant to the claims and defenses in this action.

15

14.

Facebook objects to Plaintiffs’ definition and use of the terms “You,” “Your,” or

16

“Facebook” as vague, ambiguous, overly broad, and unduly burdensome to the extent the terms are

17

meant to include “directors, officers, employees, partners, members, representatives, agents

18

(including attorneys, accountants, consultants, investment advisors or bankers), and any other person

19

purporting to act on [Facebook, Inc.’s] behalf. . . . parents, subsidiaries, affiliates, predecessor

20

entities, successor entities, divisions, departments, groups, acquired entities and/or related entities or

21

any other entity acting or purporting to act on its behalf” over which Facebook exercises no control,

22

and to the extent that Plaintiffs purport to use these terms to impose obligations that go beyond the

23

requirements of the Federal and Local Rules.
OBJECTIONS TO “RULES OF CONSTRUCTION” AND INSTRUCTIONS

24
25
26
27
28
Gibson, Dunn &
Crutcher LLP

1.

Facebook objects to Plaintiffs’ “Rules of Construction” and “Instructions” to the

extent they impose obligations that go beyond the requirements of the Federal and Local Rules.
2.

Facebook objects to Plaintiffs’ Instruction No. 2 to the extent that it is not limited to

the relevant time period, thus making the Instruction overly broad, unduly burdensome, and not
5
DEFENDANT FACEBOOK, INC.’S SUPPLEMENTAL RESPONSES AND OBJECTIONS TO
PLAINTIFFS’ NARROWED SECOND SET OF INTERROGATORIES
Case No. C 13-05996 PJH (MEJ)

1

relevant to the claims or defenses in this action. Unless otherwise specified in its responses, and

2

pursuant to the agreement of the parties, Facebook’s response will be limited to information

3

generated between April 1, 2010 and December 30, 2013.

4

3.

Facebook objects to Plaintiffs’ Instruction No. 6 as ambiguous and unduly

5

burdensome. Facebook further objects to the instruction to the extent it exceeds the requirements of

6

the Federal and Local Rules.

7

OBJECTION TO PURPORTED “RELEVANT TIME PERIOD”

8

Facebook objects to Plaintiffs’ proposed “Relevant Time Period” (September 26, 2006

9

through the present) because it substantially exceeds the proposed class period identified in Plaintiffs’

10

Consolidated Amended Complaint, does not reflect the time period that is relevant to Plaintiffs’

11

claims in this action, and renders the Interrogatories overly broad, unduly burdensome, and irrelevant.

12

Unless otherwise specified, and pursuant to the agreement of the parties, Facebook’s Responses to

13

these Interrogatories will be limited to information generated between April 1, 2010 and December

14

30, 2013. Facebook otherwise objects to the remainder of Plaintiffs’ statement regarding the

15

“Relevant Time Period” to the extent that it purports to impose obligations beyond those imposed by

16

the Federal and Local Rules.
SPECIFIC RESPONSES AND OBJECTIONS

17
18

INTERROGATORY NO. 8:

19
20

Identify all facts relating to the Processing of each Private Message sent or received by
Plaintiffs containing a URL1, including, for each Private Message:

21

(A)

all Objects that were created during the Processing of the Private Message, including

22

the (id) and the Object Type for each Object, as well as any Key -> Value Pair(s)

23

contained in each Object;

24
25
26
27

1

Each such Private Message has been identified by each Plaintiff in Exhibit 1 to his respective Objections and
Responses to Defendant’s First Set of Interrogatories.

28
6

Gibson, Dunn &
Crutcher LLP

DEFENDANT FACEBOOK, INC.’S SUPPLEMENTAL RESPONSES AND OBJECTIONS TO
PLAINTIFFS’ NARROWED SECOND SET OF INTERROGATORIES
Case No. C 13-05996 PJH (MEJ)

1

(B)

all Objects that were created specifically when the embedded URL was shared,

2

including the (id) and the Object Type for each Object, as well as any Key -> Value

3

Pair(s) contained in each Object;

4

(C)

all Associations related to each Private Message, identified by the Source Object,

5

Association Type, and Destination Object, as well as any Key -> Value Pair(s)

6

contained in each Association;

7

(D)

the database names and table names in which each Association and Object is stored;

8

(E)

each application or feature in Facebook that uses the Objects or Associations created

9
10
11
12

for each Private Message; and
(F)

how each Object associated with the Private Message was used by Facebook.

RESPONSE TO INTERROGATORY NO. 8:
Facebook restates and incorporates its Preliminary Statement, General Objections, Objections

13

to “Rules of Construction,” Instructions, and Purported “Relevant Time Period” as though fully set

14

forth in this Response. Facebook further objects to this Interrogatory on the following additional

15

grounds:

16

(A)

The Interrogatory is vague and ambiguous in its use of the terms and phrases

17

“Processing”; “Private Message”; “Objects”; “(id)”; “Object Type”; “Key -> Value Pair(s)”; “Objects

18

that were created specifically when the embedded URL was shared”; “Associations”; “Source

19

Object”; “Association Type”; “Destination Object”; “database names and table names”; and

20

“application or feature.”

21

(B)

The Interrogatory is compound.

22

(C)

The Interrogatory seeks information that is not relevant to the claims or defenses in

23

this action to the extent it concerns practices other than those challenged in this action (the alleged

24

increase in the Facebook “Like” count on a website when the URL for that website was contained in

25

a message transmitted through Facebook’s Messages product during the class period).

26

(D)

The Interrogatory is vague, unduly burdensome, and overly broad in that it purports to

27

seek “all facts relating to the Processing of each Private Message sent or received by Plaintiffs

28

containing a URL.”
7

Gibson, Dunn &
Crutcher LLP

DEFENDANT FACEBOOK, INC.’S SUPPLEMENTAL RESPONSES AND OBJECTIONS TO
PLAINTIFFS’ NARROWED SECOND SET OF INTERROGATORIES
Case No. C 13-05996 PJH (MEJ)

1
2
3

(E)

The Interrogatory seeks information that reflects trade secrets, confidential, and/or

proprietary company information.
(F)

The Interrogatory exceeds the bounds of Federal Rule of Civil Procedure 33(a)(1),

4

which provides that “a party may serve on any other party no more than 25 written interrogatories,

5

including all discrete subparts.”

6
7

Subject to and without waiving the foregoing general and specific objections, and subject to
the ongoing nature of discovery in this action, Facebook responds as follows:

8

Facebook refers Plaintiffs to Facebook’s Responses and Objections to Plaintiffs’ Interrogatory

9

Nos. 2, 3, and 4. Facebook also will meet and confer with Plaintiffs’ counsel to determine the proper

10

scope of this overly broad and ambiguous Interrogatory.

11

SUPPLEMENTAL RESPONSE TO INTERROGATORY NO. 8:

12

Facebook restates and incorporates its Preliminary Statement, General Objections, Objections

13

to “Rules of Construction,” Instructions, and Purported “Relevant Time Period” as though fully set

14

forth in this Response. Facebook further objects to this Interrogatory on the following additional

15

grounds:

16

(A)

The Interrogatory is vague and ambiguous in its use of the terms and phrases

17

“Processing”; “Private Message”; “Objects”; “(id)”; “Object Type”; “Key -> Value Pair(s)”; “Objects

18

that were created specifically when the embedded URL was shared”; “Associations”; “Source

19

Object”; “Association Type”; “Destination Object”; “database names and table names”; and

20

“application or feature.”

21

(B)

The Interrogatory is compound.

22

(C)

The Interrogatory seeks information that is not relevant to the claims or defenses in

23

this action to the extent it concerns practices other than those challenged in this action (the alleged

24

increase in the Facebook “Like” count on a website when the URL for that website was contained in

25

a message transmitted through Facebook’s Messages product during the class period).

26

(D)

The Interrogatory is vague, unduly burdensome, and overly broad in that it purports to

27

seek “all facts relating to the Processing of each Private Message sent or received by Plaintiffs

28

containing a URL.”
8

Gibson, Dunn &
Crutcher LLP

DEFENDANT FACEBOOK, INC.’S SUPPLEMENTAL RESPONSES AND OBJECTIONS TO
PLAINTIFFS’ NARROWED SECOND SET OF INTERROGATORIES
Case No. C 13-05996 PJH (MEJ)

1
2
3

(E)

The Interrogatory seeks information that reflects trade secrets, confidential, and/or

proprietary company information.
(F)

The Interrogatory exceeds the bounds of Federal Rule of Civil Procedure 33(a)(1),

4

which provides that “a party may serve on any other party no more than 25 written interrogatories,

5

including all discrete subparts.”

6
7
8
9

Subject to and without waiving the foregoing general and specific objections, and subject to
the ongoing nature of discovery in this action, Facebook responds as follows:
Facebook refers Plaintiffs to Facebook’s Responses and Objections to Plaintiffs’ Interrogatory
Nos. 2, 3, and 4. Additionally, and pursuant to Rule 33(d) of the Federal Rules of Civil Procedure,

10

Facebook refers Plaintiffs to documents bearing production numbers FB000005502 through

11

FB000006175, which contain information responsive to this Interrogatory for the messages identified

12

in Plaintiffs’ letter of July 24, 2015 that could be located after a reasonable search and diligent

13

inquiry. The chart attached as Exhibit 1 identifies the production numbers of the documents that

14

correspond to the messages identified in Plaintiffs’ July 24, 2015 letter.

15

DATED: September 1, 2015

16
17
18

GIBSON, DUNN & CRUTCHER LLP
By:

/s/
Joshua A. Jessen

Attorneys for Defendant FACEBOOK, INC.

19
20
21
22
23
24
25
26
27
28
9

Gibson, Dunn &
Crutcher LLP

DEFENDANT FACEBOOK, INC.’S SUPPLEMENTAL RESPONSES AND OBJECTIONS TO
PLAINTIFFS’ NARROWED SECOND SET OF INTERROGATORIES
Case No. C 13-05996 PJH (MEJ)

Exhibit 1

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY
To

From

Date

URL

1

Production Number(s)
FB000005502-FB000005527
FB000005528-FB000005574
FB000005575-FB000005576

2

FB000005577-FB000005578

3

FB000005579-FB000005600
FB000005601-FB000005646
FB000005647-FB000005648

4

FB000005649-FB000005672
FB000005673-FB000005719
FB000005720-FB000005721

5

FB000005722-FB000005749
FB000005750-FB000005797
FB000005798-FB000005799

6

FB000005800-FB000005801

7

FB000005802-FB000005826
FB000005827-FB000005879
FB000005880-FB000005881

10

Unavailable.

68

FB000005882-FB000005883

1

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY
To

From

Date

URL

89

Production Number(s)
FB000005884-FB000005886
FB000005887-FB000005932
FB000005933-FB000005934

93

FB000005935-FB000005957
FB000005958-FB000006004
FB000006005-FB000006006

99

FB000006007-FB000006008

113

FB000006009-FB000006037
FB000006038-FB000006084
FB000006085-FB000006087

115

Unavailable.

123

FB000006088-FB000006089

200

FB000006090-FB000006119
FB000006120-FB000006169
FB000006170-FB000006171

410

Unavailable.

2

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY
To

From

Date

URL

Production Number(s)

654

FB000006172-FB000006173

482

FB000006174-FB000006175

3

1

PROOF OF SERVICE

2
3
4

I, Ashley M. Rogers, declare as follows:
I am employed in the County of Santa Clara, State of California, I am over the age of eighteen
years and am not a party to this action; my business address is 1881 Page Mill Road, Palo Alto, CA
94304-1211, in said County and State. On September 1, 2015, I served the following document(s):

5

DEFENDANT FACEBOOK, INC.’S SUPPLEMENTAL RESPONSES AND
OBJECTIONS TO PLAINTIFFS’ NARROWED SECOND SET OF
INTERROGATORIES

6
7

on the parties stated below, by the following means of service:

8

David F. Slade
[e-mail]
James Allen Carney
[e-mail]
Joseph Henry Bates, III
Carney Bates & Pulliam, PLLC
[e-mail]

9
10
11
12
13

Melissa Ann Gardner
[e-mail]
Nicholas Diamand
[e-mail]
Rachel Geman
[e-mail]
Michael W. Sobol
Lieff Cabraser Heimann & Bernstein, LLP
[e-mail]

14
15
16
17
18
19



BY ELECTRONIC SERVICE: On the above-mentioned date, based on a court order or
an agreement of the parties to accept service by electronic transmission, I caused the
documents to be sent to the persons at the electronic notification addresses as shown
above.



I am employed in the office of Joshua A. Jessen and am a member of the bar of this court.



I declare under penalty of perjury that the foregoing is true and correct.

20
21
22
23
24

Executed on September 1, 2015.

25
26

/s/
Ashley M. Rogers

27
28
Gibson, Dunn &
Crutcher LLP

DEFENDANT FACEBOOK, INC.’S SUPPLEMENTAL RESPONSES AND OBJECTIONS TO
PLAINTIFFS’ NARROWED SECOND SET OF INTERROGATORIES
Case No. C 13-05996 PJH (MEJ)

EXHIBIT 21

EXHIBIT 22

EXHIBIT 23

EXHIBIT 24

EXHIBIT 25

EXHIBIT 26

EXHIBIT 27
FILED UNDER SEAL

EXHIBIT 28
FILED UNDER SEAL

EXHIBIT 29
FILED UNDER SEAL

EXHIBIT 30
FILED UNDER SEAL

EXHIBIT 31

1
2
3
4
5
6
7
8
9
10
11

Michael W. Sobol (State Bar No. 194857)
[e-mail]
Melissa Gardner (State Bar No. 289096)
[e-mail]
LIEFF CABRASER HEIMANN & BERNSTEIN, LLP
275 Battery Street, 29th Floor
San Francisco, CA 94111-3339
Telephone: [phone]
Facsimile: 415.956.1008
Rachel Geman
[e-mail]
Nicholas Diamand
[e-mail]
LIEFF CABRASER HEIMANN & BERNSTEIN, LLP
250 Hudson Street, 8th Floor
New York, NY 10013-1413
Telephone: [phone]
Facsimile: 212.355.9592

16

Patrick V. Dahlstrom
[e-mail]
POMERANTZ, LLP
10 S. La Salle Street Suite 3505
Chicago, Illinois 60603
Telephone: [phone]
Facsimile: 312.377.1184

Hank Bates (State Bar No. 167688)
[e-mail]
Allen Carney
[e-mail]
David Slade
[e-mail]
CARNEY BATES & PULLIAM, PLLC
11311 Arcade Drive
Little Rock, AR 72212
Telephone: [phone]
Facsimile: 501.312.8505

17

Jeremy A. Lieberman
Lesley F. Portnoy
[e-mail]
POMERANTZ, LLP
600 Third Avenue, 20th Floor
New York, New York 10016
Telephone: [phone]
Facsimile: 212.661.8665

Attorneys Plaintiffs and the Proposed Class

12
13
14
15

18

UNITED STATES DISTRICT COURT

19

NORTHERN DISTRICT OF CALIFORNIA

20
21
22

MATTHEW CAMPBELL, MICHAEL
HURLEY, and DAVID SHADPOUR, on
behalf of themselves and all others
similarly situated,

23

Case No. C 13-5996 PJH
PLAINTIFFS’ FIRST SET OF REQUESTS
FOR PRODUCTION OF DOCUMENTS TO
DEFENDANT

Plaintiffs,
24
v.
25
FACEBOOK, INC.,
26
Defendant.
27
28
1215231.1

PLAINTIFFS’ FIRST SET OF REQUESTS FOR
PRODUCTION OF DOCUMENTS TO DEFENDANT
CASE NO. C 13-5996 PJH

1

Pursuant to Rules 26 and 34 of the Federal Rules of Civil Procedure, the Plaintiffs request

2

that Defendant Facebook respond to the following requests for the production of Documents

3

(each, a “Request,” collectively the “Requests”) within thirty (30) days of service.

4
5

DEFINITIONS
(a)

6
7

Inc.; Case No. C 13-5996 PJH (N. Dist. Cal.).
(b)

8
9

“Action” means the case captioned Matthew Campbell and Michael Hurley v. Facebook,

“Active Likes” means any Likes that were generated by Facebook Users affirmatively
clicking on a Like button Social PlugIn.

(c)

“Architecture” refers to each piece of Facebook infrastructure – including but not limited

10

to source code, software, applications, web crawlers, hardware, and networks – utilized to

11

implement or otherwise facilitate any of Your services.

12

(d)

“Communication” means the conveyance (in the form of facts, ideas, thoughts, opinions,

13

data, inquiries or otherwise) of information and includes, without limitation,

14

correspondence, memoranda, reports, presentations, face-to-face conversations, telephone

15

conversations, text messages, instant messages, voice messages, negotiations, agreements,

16

inquiries, understandings, meetings, letters, notes, telegrams, mail, email, and postings of

17

any type.

18

(e)

“Complaint” means the operative Complaint in this Action.

19

(f)

“Developer(s)” means Third Parties who utilize the Facebook platform to either build

20

their own applications or to incorporate the Facebook platform into their own products

21

(e.g., incorporating Facebook’s Like Social PlugIn into a website).

22

(g)

“Document(s)” means all materials within the full scope of Fed. R. Civ. P. 34 including

23

but not limited to: all writings and recordings, including the originals, drafts and all non-

24

identical copies, whether different from the original by reason of any notation made on

25

such copies or otherwise (including but without limitation to, email and attachments,

26

correspondence, memoranda, notes, diaries, statistics, letters, telegrams, minutes,

27

contracts, reports, studies, checks, statements, tags, labels, invoices, brochures,

28

periodicals, receipts, returns, summaries, pamphlets, books, interoffice and intra-office
1215231.1

-2-

PLAINTIFFS’ FIRST SET OF REQUESTS FOR
PRODUCTION OF DOCUMENTS TO DEFENDANT
CASE NO. C 13-5996 PJH

1

Communications, instant messages, chats, offers, notations of any sort of conversations,

2

working papers, applications, permits, file wrappers, indices, telephone calls, meetings or

3

printouts, teletypes, telefax, invoices, worksheets, and all drafts, alterations, modifications,

4

changes and amendments of any of the foregoing), graphic or aural representations of any

5

kind (including without limitation, photographs, charts, microfiche, microfilm, videotape,

6

recordings, motion pictures, plans, drawings, surveys), and electronic, mechanical,

7

magnetic, optical or electric records or representations of any kind (including without

8

limitation, computer files and programs, tapes, cassettes, discs, recordings), including

9

Metadata.

10

(h)

“Electronic Media” means any magnetic, optical, or other storage media device used to

11

record or access ESI including, without limitation, computer memory, hard disks, floppy

12

disks, flash memory devices, CDs, DVDs, Blu-ray disks, cloud storage (e.g., DropBox,

13

Box, OneDrive, and SharePoint), tablet computers (e.g., iPad, Kindle, Nook, and Samsung

14

Galaxy), cellular or smart phones (e.g., BlackBerry, iPhone, Samsung Galaxy), personal

15

digital assistants, magnetic tapes of all types or any other means for digital storage and/or

16

transmittal.

17

(i)

“ESI” or “Electronically Stored Information” refers to information and Documents (as

18

defined within this section) within the full scope of Fed. R. Civ. P. 34 – with all Metadata

19

intact – created, manipulated, communicated, stored, and best utilized in digital form, and

20

requiring the use of Electronic Media to access. Such information includes emails, email

21

attachments, message boards, forums, support tickets, support articles, security alerts,

22

pop-ups, videos, discussion boards, data, charts, BETA results, error messages, bug

23

reports, source code, investigative reports, monitoring reports, comments, press releases,

24

drafts, models, templates, websites, instant messages, chats, and intercompany and intra-

25

company Communications.

26

(j)

“Facebook User(s)” means Persons who have established a Facebook account.

27

(k)

“Facebook User Data Profile(s)” means the group of data points, collected by You from

28

any source and assigned by You to specific Facebook Users, for purposes including but
1215231.1

-3-

PLAINTIFFS’ FIRST SET OF REQUESTS FOR
PRODUCTION OF DOCUMENTS TO DEFENDANT
CASE NO. C 13-5996 PJH

1

not limited to “bundling characteristics” and determining the potential interests of

2

Facebook Users as described in Your Data Use Policy under the heading “How

3

Advertising and Sponsored Stories Work.”

4

(l)

“Identify,” with respect to Documents, means to give, to the extent known, the (a) type

5

of Document; (b) general subject matter; (c) date of the Document; (d) author(s), (e)

6

addressee(s), and (f) recipient(s).

7

(m)

“Identify,” with respect to Persons, means to give, to the extent known, the Person’s full

8

name, present or last known address, and when referring to a natural person, additionally,

9

the present or last known place of employment. Once a Person has been identified in

10

accordance with this subparagraph, only the name of that Person need be listed in

11

response to subsequent discovery requesting the identification of that Person.

12

(n)

“Including” means “including but not limited to” and “including without limitation.”

13

(o)

“Metadata” refers to structured information about an electronic file that is embedded in

14
15

the file, describing the characteristics, origins, usage and validity the electronic file.
(p)

“Meeting” means the contemporaneous presence, whether in person or through any

16

means of communication, of any natural persons, whether or not such presence was by

17

chance or prearranged, and whether or not the meeting was formal or informal, or

18

occurred in connection with some other activity.

19

(q)

“Motion to Dismiss” means Your motion to dismiss filed in this Action (Docket No. 29).

20

(r)

“Native Format” refers to the original file format in which a particular Document or item

21
22

of ESI was created.
(s)

“Passive Likes” means any Likes that were not generated by Facebook Users

23

affirmatively clicking on a Like button Social PlugIn, and were instead generated as a

24

result of Facebook scanning URLs contained within Private Message (i.e., generated

25

through the behavior described in the Wall Street Journal article “How Private Are Your

26

Private Facebook Messages”).

27

(t)

28

“Person” means any natural person or any business, legal or governmental entity or
association.

1215231.1

-4-

PLAINTIFFS’ FIRST SET OF REQUESTS FOR
PRODUCTION OF DOCUMENTS TO DEFENDANT
CASE NO. C 13-5996 PJH

1

(u)

“Plaintiff” and “Plaintiffs” refer to the named plaintiffs in this Action, and any reference

2

to “Plaintiff” or “Plaintiffs” shall be construed disjunctively or conjunctively as necessary

3

in order to bring within the scope of the request all responses which otherwise might be

4

construed to be outside its scope.

5

(v)

“Private Message(s)” means the portion of Facebook’s service designed to transmit

6

private messages between users – as opposed to posts – and which process is engaged by,

7

inter alia, the “Message” button on users’ profile pages or via the Messenger app.

8

(w)

9

could in any way apprise its possessor of any substance, meaning, or purport of the Private

10
11

“Private Message Content” means any data or metadata related to a Private Message that

Message.
(x)

“Private Message Transmission” means the act or series of acts taken by Facebook

12

during the exchange of Private Messages between Facebook Users; beginning the moment

13

a Facebook User initiates the process of composing a Private Message to at least one

14

recipient Facebook User, and ending once the recipient(s) view(s) the Private Message.

15

Such act or acts include routing, delivery, processing, scanning, anti-virus and spam

16

filtration, writing of the Private Message to any server, analysis, content extraction,

17

generation of data, and generation of metadata.

18

(y)

19
20

“Process” refers to a series of discrete steps, ordered and undertaken to achieve a specific
goal or set of goals that facilitate Facebook’s operation.

(z)

“Relate(s) o,” “Related to” or “Relating to” shall be construed to mean referring to,

21

reflecting, concerning, pertaining to or in any manner being connected with the matter

22

discussed.

23

(aa)

“Targeted Advertising” means advertising purchased by Third Parties, to be delivered

24

by You to Facebook Users based upon inferences drawn from data points within Facebook

25

Users’ Data Profiles (e.g., “location,” “demographics,” “interests,” and “behaviors,” as

26

described on Your website on the page titled “How to target Facebook Ads;

27

https://www.facebook.com/business/a/online-sales/ad-targeting-details).

28

(bb)
1215231.1

“Third Party” refers to any party other than You or Plaintiffs.
-5-

PLAINTIFFS’ FIRST SET OF REQUESTS FOR
PRODUCTION OF DOCUMENTS TO DEFENDANT
CASE NO. C 13-5996 PJH

1

(cc)

“Transmission,” “Transmit,” and “Transmitting” refer to any intentional act by one

2

party which results in the possession, by at least one other party, of a Document or item of

3

ESI. Such acts include but are not limited to mailing (via the U.S. Post Office or other

4

Third Party carriers such as FedEx or UPS), faxing, emailing, hand-delivering, and

5

causing to be delivered via courier service any Document and/or, where applicable, item

6

of ESI.

7

(dd)

“You,” “Your,” and “Facebook” shall mean Facebook, Inc. and any of its directors,

8

officers, employees, partners, members, representatives, agents (including attorneys,

9

accountants, consultants, investment advisors or bankers), and any other person purporting

10

to act on its behalf. In the case of business entities, these defined terms include parents,

11

subsidiaries, affiliates, predecessor entities, successor entities, divisions, departments,

12

groups, acquired entities and/or related entities or any other entity acting or purporting to

13

act on its behalf.

14

RULES OF CONSTRUCTION

15

1.

The connectives “and” and “or” shall be construed either disjunctively or

16

conjunctively as necessary to bring within the scope of the discovery request all responses that

17

might otherwise be construed to be outside of its scope.

18

2.

“Any,” “all,” and “each” shall be construed as any, all and each.

19

3.

The singular form of a noun or pronoun includes the plural form and vice versa.

20

4.

The use of any tense of any verb shall also include within its meaning all other

21

tenses of that verb.

22
23

5.

A term or word defined herein is meant to include both the lower and upper case

reference to such term or word.

24

6.

Any headings which appear in the Requests for Production section have been

25

inserted for the purpose of convenience and ready reference. They do not purport to, and are not

26

intended to, define, limit, or extend the scope or intent of the Requests to which they pertain.

27
28
1215231.1

-6-

PLAINTIFFS’ FIRST SET OF REQUESTS FOR
PRODUCTION OF DOCUMENTS TO DEFENDANT
CASE NO. C 13-5996 PJH

1

INSTRUCTIONS
1.

2

You are requested to produce all Documents and ESI in Your possession, custody,

3

or control – as well as Documents and ESI that are in the possession of Your partners, officers,

4

employees, attorneys, accountants, representatives, or agents, or that are otherwise subject to

5

Your custody or control – that are described below.
2.

6

Unless otherwise indicated, the Documents and ESI to be produced include all

7

Documents and ESI prepared, sent, dated or received, or those that otherwise came into existence

8

any time during the Relevant Time Period.
3.

9

The production by one person, party, or entity of a Document or item of ESI does

10

not relieve another person, party, or entity from the obligation to produce his, her, or its own copy

11

of that Document or ESI, even if the two are identical.
4.

12

In producing Documents and ESI, You are requested to produce a copy of each

13

original Document and ESI together with a copy of all non-identical copies and drafts of that

14

Document. If the original of any Document and ESI cannot be located, a copy shall be provided

15

in lieu thereof, and shall be legible and bound or stapled in the same manner as the original.
5.

16

Documents and ESI shall be produced as they are kept in the usual course of

17

business. All Documents and ESI shall be produced with a copy of the file folder, envelope, or

18

other container in which the Documents and ESI are kept or maintained. All Documents and ESI

19

shall be produced intact in their original files, without disturbing the organization of Documents

20

and ESI employed during the conduct of the ordinary course of business and during the

21

subsequent maintenance of the Documents and ESI.
6.

22

Documents and ESI not otherwise responsive to this discovery request shall be

23

produced if such Documents and ESI mention, discuss, refer to, or explain the Documents and

24

ESI which are called for by this discovery request, or if such Documents and ESI are attached to

25

Documents and ESI called for by this discovery request and constitute routing slips, transmittal

26

memoranda, or letters, comments, evaluations or similar materials.
7.

27
28

Each Document and item of ESI requested herein is requested to be produced in its

entirety and without deletion or excisions, regardless of whether You consider the entire
1215231.1

-7-

PLAINTIFFS’ FIRST SET OF REQUESTS FOR
PRODUCTION OF DOCUMENTS TO DEFENDANT
CASE NO. C 13-5996 PJH

1

Document or item of ESI to be relevant or responsive to this request. If You have redacted any

2

portion of a Document or item of ESI, stamp the word “redacted” on each page of the Document

3

or item of ESI that You have redacted.

4

8.

If any Document or item of ESI called for by these requests is not produced in full

5

or is redacted on the ground that it is privileged or otherwise claimed to be protected against

6

production, You are requested to provide the following information with respect to each such

7

Document or item of ESI or redaction:

8

(a)

its date;

9

(b)

its author(s), its signatory(s) and each and every other person who prepared

10

or participated in its preparation;

11

(c)

the type of Document or item of ESI it is (e.g., letter, chart, memorandum,

13

(d)

a description of its subject matter and length;

14

(e)

a list of those persons and entities to whom said Document(s) or item of

12

etc.);

15

ESI was disseminated, together with their last known addresses and the date or approximate date

16

on which each such person or entity received it;

17

(f)

a list of all other persons to whom the contents of the Document or item of

18

ESI have been disclosed, the date such disclosure took place, the means of such disclosure, and

19

the present location of the Document or item of ESI and all copies thereof;

20

(g)

21

of ESI and all copies thereof; and

22

(h)

23

each and every person having custody or control of the Document or item

the nature of the privilege or other rule of law relied upon and any facts

supporting Your position in withholding production of each such Document or item of ESI.

24

9.

If You assert an objection to any request, You must nonetheless respond and

25

produce any responsive Documents and ESI that are not subject to the stated objection. If You

26

object to part of a request or category, You must specify the portion of the request to which You

27

object, and must produce Documents and ESI responsive to the remaining parts of the request.

28
1215231.1

-8-

PLAINTIFFS’ FIRST SET OF REQUESTS FOR
PRODUCTION OF DOCUMENTS TO DEFENDANT
CASE NO. C 13-5996 PJH

1

10.

Notwithstanding a claim that a Document or item of ESI is protected from

2

disclosure, any Document or item of ESI so withheld must be produced with the portion claimed

3

to be protected redacted.

4

11.

If any Document or ESI is known to have existed but no longer exists, has been

5

destroyed, or is otherwise available, You must identify the Document or ESI, the reason for its

6

loss, destruction or unavailability, the name of each person known or reasonably believed by You

7

to have present possession, custody, or control of the original and any copy thereof (if

8

applicable), and a description of the disposition of each copy of the Document or ESI.

9

12.

Every Request for Production herein shall be deemed a continuing discovery

10

request, and You are to supplement information which adds to or is in any way inconsistent with

11

Your initial answers to these Requests.

12

13.

Plaintiffs reserve the right to propound additional discovery requests.

13

RELEVANT TIME PERIOD

14

The relevant time period for each Document Request is for September 26, 2006 through

15

the present (the “Relevant Time Period”), unless otherwise specifically indicated, and shall

16

include all Documents, ESI, and any other information that relate to such period, even though

17

prepared or published outside of the relevant time period. If a Document or item of ESI prepared

18

before this period is necessary for a correct or complete understanding of any Document or item

19

of ESI covered by a request, You must produce the earlier or subsequent Document or item of

20

ESI as well. If any Document or item of ESI is undated and the date of its preparation cannot be

21

determined, the Document or item of ESI shall be produced if otherwise responsive to the

22

production request.

23

REQUESTS FOR PRODUCTION OF DOCUMENTS

24

A.

25
26

REQUEST FOR PRODUCTION NO. 1:

27
28

Requests Related to Facebook’s Corporate Organizational Structure and
Individuals Who May Possess Relevant Information

All Documents and ESI showing Facebook’s organizational structure that identify all
current or former Persons at Facebook (including directors, officers, employees, or contractors)
1215231.1

-9-

PLAINTIFFS’ FIRST SET OF REQUESTS FOR
PRODUCTION OF DOCUMENTS TO DEFENDANT
CASE NO. C 13-5996 PJH

1

who may possess knowledge relevant to this Action.

2

REQUEST FOR PRODUCTION NO. 2:

3

Documents and ESI sufficient to identify all databases, networks, or any other repositories

4

of information under Your control that may contain Documents and ESI relevant to this Action.

5

REQUEST FOR PRODUCTION NO. 3:

6

Documents and ESI sufficient to identify all methods and media utilized by Your

7

employees for inter-office (internal) Communication in the course of their work, including but not

8

limited to inter-office mail (electronic and physical), reports (electronic and physical), chats, and

9

video chats, as well as how and where such Communications are stored.

10
11

B.

Requests Related to Private Message Transmission and the Like Social PlugIn

REQUEST FOR PRODUCTION NO. 4:

12

All Documents and ESI sufficient to identify each Process and/or piece of Architecture

13

involved in Private Message Transmission.

14

REQUEST FOR PRODUCTION NO. 5:

15

All Documents and ESI related to each Process and/or piece of Architecture involved in

16

the scanning of Private Message Content for purposes of creating, augmenting, or otherwise

17

maintaining Facebook User Data Profiles.

18

REQUEST FOR PRODUCTION NO. 6:

19

All Documents and ESI related to each Process and/or piece of Architecture involved in

20

the acquisition of data, metadata, or other content from Private Messages, for purposes of

21

creating, augmenting, or otherwise maintaining Facebook User Data Profiles.

22

REQUEST FOR PRODUCTION NO. 7:

23

All Documents and ESI sufficient to identify each Process and/or piece of Architecture

24

involved in spam filtering.

25

REQUEST FOR PRODUCTION NO. 8:

26
27

All Documents and ESI sufficient to identify each Process and/or piece of Architecture
involved in malware filtering.

28
1215231.1

- 10 -

PLAINTIFFS’ FIRST SET OF REQUESTS FOR
PRODUCTION OF DOCUMENTS TO DEFENDANT
CASE NO. C 13-5996 PJH

1

REQUEST FOR PRODUCTION NO. 9:

2

All Documents and ESI sufficient to identify each Process and/or piece of Architecture

3

involved in generating thumbnail/URL previews.

4

REQUEST FOR PRODUCTION NO. 10:

5

All Documents and ESI sufficient to identify each Process and/or piece of Architecture

6

involved in storing Private Messages for Facebook Users’ future review, or for any other purpose.

7

REQUEST FOR PRODUCTION NO. 11:

8

All Documents and ESI sufficient to identify each Process and/or piece of Architecture

9

involved in “protect[ing] users, the product, and the site from threats and abusive behavior,” as

10

described on page 11 of Your Motion to Dismiss.

11

REQUEST FOR PRODUCTION NO. 12:

12

All Documents and ESI sufficient to identify each Process and/or piece of Architecture

13

related to the Like Social PlugIn.

14

REQUEST FOR PRODUCTION NO. 13:

15

All Documents and ESI relating to each Process and/or piece of Architecture involved in

16

generating Passive Likes, including all Documents and ESI related to Your cessation of the

17

practice of generating Passive Likes.

18

REQUEST FOR PRODUCTION NO. 14:

19

All Documents and ESI relating to the “bug…where at times the count for the Share or

20

Like goes up by two,” identified by You in Your statement quoted in the Wall Street Journal

21

Article titled “How Private Are Your Private Facebook Messages?” and published in

22

October, 2012.

23

REQUEST FOR PRODUCTION NO. 15:

24

All Documents and ESI sufficient to identify each Process and/or piece of Architecture

25

involved in generating Active Likes.

26

REQUEST FOR PRODUCTION NO. 16:

27
28

All Documents and ESI relating to how Third Parties acquire information related to
Facebook Users from the Like Social PlugIn, including information acquired by Third Parties
1215231.1

- 11 -

PLAINTIFFS’ FIRST SET OF REQUESTS FOR
PRODUCTION OF DOCUMENTS TO DEFENDANT
CASE NO. C 13-5996 PJH

1

when a Facebook User engages the Like Social PlugIn either via Passive Likes or Active Likes.

2

REQUEST FOR PRODUCTION NO. 17:

3

All Documents and ESI relating to how Third Parties can use information related to

4

Facebook Users from the Like Social PlugIn, including Social Graph searches of data acquired

5

through Passive Likes or Active Likes.

6

C.

7
8

Requests Related to How Facebook User Data Profiles Are Created,
Augmented, and Maintained

REQUEST FOR PRODUCTION NO. 18:

9

All Documents and ESI sufficient to identify each Process and/or piece of Architecture

10

involved in the creation, augmentation, or maintenance of Facebook User Data Profiles.

11

REQUEST FOR PRODUCTION NO. 19:

12

All Documents and ESI relating to how You use any Private Message Content, including

13

for purposes related to Facebook User Profiles and/or Targeted Advertising.

14

REQUEST FOR PRODUCTION NO. 20:

15

All Documents and ESI relating to the extent to which You allow Third Parties any access

16

to any Private Message Content.

17

REQUEST FOR PRODUCTION NO. 21:

18

All Documents and ESI relating to the use of Passive Likes – or any data, metadata, or

19

other information generated therefrom – as data points in Facebook User Data Profiles.

20

REQUEST FOR PRODUCTION NO. 22:

21

All Documents and ESI relating to the use of Passive Likes – or any data, metadata, or

22

other information generated therefrom – for purposes related to Targeted Advertising.

23

REQUEST FOR PRODUCTION NO. 23:

24

All Documents and ESI relating to the use of Active Likes – or any data, metadata, or

25

other information generated therefrom – as data points in Facebook User Data Profiles.

26

REQUEST FOR PRODUCTION NO. 24:

27
28

All Documents and ESI relating to the use of Active Likes – or any data, metadata, or
other information generated therefrom – for purposes related to Targeted Advertising.
1215231.1

- 12 -

PLAINTIFFS’ FIRST SET OF REQUESTS FOR
PRODUCTION OF DOCUMENTS TO DEFENDANT
CASE NO. C 13-5996 PJH

1
2

D.

Requests Related to How Facebook Obtains Consent

REQUEST FOR PRODUCTION NO. 25:
All Documents and ESI used by You to establish Facebook Users’ express consent to the

3
4

practices forming the basis for Plaintiffs’ Complaint.

5

REQUEST FOR PRODUCTION NO. 26:
All Documents and ESI supporting the position advanced in pages 18-19 of Your Motion

6
7

to Dismiss that Facebook Users impliedly consent to the practices forming the basis for Plaintiffs’

8

Complaint.

9

E.

Requests Related to Law Enforcement Investigations, Media Investigations,
and Complaints Involving Privacy Issues

10
11

REQUEST FOR PRODUCTION NO. 27:

12

All Documents and ESI related to investigations of Facebook by any governmental

13

agency (in the United States or otherwise), regulatory agency, law enforcement agency, or

14

advisory council relating to user privacy issues, including investigations by United States Federal

15

Trade Commission and the Office of the Irish Data Protection Commissioner.

16

REQUEST FOR PRODUCTION NO. 28:
All Documents and ESI related to FTC MATTER/FILE NUMBER: 092 3184, In the

17
18

Matter of Facebook, Inc., a corporation, including all Documents and ESI related to

19

implementation of the business practice changes mandated by the FTC in its July 27, 2012

20

Decision and Order (“FTC Order”), and including all Documents and ESI related to the Third

21

Party, biennial assessments and reports identified on pages 6 and 7 of the FTC Order.

22

REQUEST FOR PRODUCTION NO. 29:
All Documents and ESI related to – and sufficient to identify – the “dedicated team of

23
24

privacy professionals” identified on page 8 of Your Form 10-K for fiscal year ending

25

December 31, 2013, including any involvement such Persons had in matters related to (1)

26

obtaining consent of Facebook Users for Your practices implicating privacy and data use; (2)

27

Private Messages; and (3) the acts and practices described in the Complaint.

28
1215231.1

- 13 -

PLAINTIFFS’ FIRST SET OF REQUESTS FOR
PRODUCTION OF DOCUMENTS TO DEFENDANT
CASE NO. C 13-5996 PJH

1

REQUEST FOR PRODUCTION NO. 30:

2

All Documents and ESI related to all audits of Facebook conducted by the Office of the

3

Irish Data Protection Commissioner.

4

REQUEST FOR PRODUCTION NO. 31:

5

All Documents and ESI related to Third Parties discussing Passive Likes, including the

6

Wall Street Journal article “How Private Are Your Private Facebook Messages,” the Digital

7

Trends article “Facebook Scans Private Messages for Brand Page Mentions, Admits a Bug is

8

Boosting Likes,” and the Hacker News post “Facebook Graph API exploit that let’s [sic] you

9

pump up to 1800 ‘Likes’ in an hour.”

10
11

F.

Miscellaneous Requests

REQUEST FOR PRODUCTION NO. 32:

12

All Documents and ESI that You contend evidence or substantiate Your defenses in this

13

Action.

14

REQUEST FOR PRODUCTION NO. 33:

15

All Documents and ESI related to Your policies, practices, or procedures, if any,

16

regarding the retention or destruction of Documents and files, including emails, email backup or

17

archive tapes, hard drives, and corporate storage, including, without limitation, any changes or

18

modifications in such policies or practices during the Relevant Time Period.

19

REQUEST FOR PRODUCTION NO. 34:

20

All insurance policies, including any declaration pages and riders, which could be used to

21

satisfy any claim in this action.

22

REQUEST FOR PRODUCTION NO. 35:

23

A plain-English description or glossary for any and all lists, legends, codes, abbreviations,

24

collector initials, or other non-obvious terms, words, or data contained in any of the Documents

25

or ESI produced in response to any of these Requests for Production, and to the extent applicable,

26

with any of the Interrogatories served herewith.

27

REQUEST FOR PRODUCTION NO. 36:

28

For any source code related to any of these Requests, Documents and ESI sufficient to
1215231.1

- 14 -

PLAINTIFFS’ FIRST SET OF REQUESTS FOR
PRODUCTION OF DOCUMENTS TO DEFENDANT
CASE NO. C 13-5996 PJH

1

identify all code repositories for such source code.

2

REQUEST FOR PRODUCTION NO. 37:

3

For any source code related to any of these Requests, check in/check out histories –

4

including timestamps, version numbers, and usernames – for such source code.

5

REQUEST FOR PRODUCTION NO. 38:

6

All Documents and ESI related to any Facebook User complaints related to the practices

7

alleged in Plaintiffs’ Complaint, as well as all responses from Facebook thereto.

8

REQUEST FOR PRODUCTION NO. 39:

9

All Documents and ESI related to Your representations to Third Parties regarding the use

10

of Active and Passive Likes in marketing and/or Targeted Advertising, including but not limited

11

to form contracts, marketing materials, and internal memoranda describing the purported benefits

12

of Active and Passive Likes to Third Parties.

13

REQUEST FOR PRODUCTION NO. 40:

14

All Documents and ESI related to each Plaintiff.

15
16
17
18
19
20
21
22
23
24
25
26
27
28
1215231.1

- 15 -

PLAINTIFFS’ FIRST SET OF REQUESTS FOR
PRODUCTION OF DOCUMENTS TO DEFENDANT
CASE NO. C 13-5996 PJH

1

Dated: January 26, 2015

2

Respectfully submitted,
LIEFF CABRASER HEIMANN & BERNSTEIN, LLP

3
4

By:

5

Michael W. Sobol (State Bar No. 194857)
[e-mail]
Melissa Gardner (State Bar No. 289096)
[e-mail]
LIEFF CABRASER HEIMANN & BERNSTEIN, LLP
275 Battery Street, 29th Floor
San Francisco, CA 94111-3339
Telephone: [phone]
Facsimile: 415.956.1008

6
7
8
9
10

/s/ Michael W. Sobol

Rachel Geman
[e-mail]
Nicholas Diamand
[e-mail]
LIEFF CABRASER HEIMANN & BERNSTEIN, LLP
250 Hudson Street, 8th Floor
New York, NY 10013-1413
Telephone: [phone]
Facsimile: 212.355.9592

11
12
13
14
15

20

Hank Bates (State Bar No. 167688)
[e-mail]
Allen Carney
[e-mail]
David Slade
[e-mail]
CARNEY BATES & PULLIAM, PLLC
11311 Arcade Drive
Little Rock, AR 72212
Telephone: [phone]
Facsimile: 501.312.8505

21

Attorneys for Plaintiffs and the Proposed Class

16
17
18
19

22
23
24
25
26
27
28
1215231.1

- 16 -

PLAINTIFFS’ FIRST SET OF REQUESTS FOR
PRODUCTION OF DOCUMENTS TO DEFENDANT
CASE NO. C 13-5996 PJH

1
2
3
4
5
6
7
8
9
10
11

Michael W. Sobol (State Bar No. 194857)
[e-mail]
Melissa Gardner (State Bar No. 289096)
[e-mail]
LIEFF CABRASER HEIMANN & BERNSTEIN, LLP
275 Battery Street, 29th Floor
San Francisco, CA 94111-3339
Telephone: [phone]
Facsimile: 415.956.1008
Rachel Geman
[e-mail]
Nicholas Diamand
[e-mail]
LIEFF CABRASER HEIMANN & BERNSTEIN, LLP
250 Hudson Street, 8th Floor
New York, NY 10013-1413
Telephone: [phone]
Facsimile: 212.355.9592

16

Patrick V. Dahlstrom
[e-mail]
POMERANTZ, LLP
10 S. La Salle Street Suite 3505
Chicago, Illinois 60603
Telephone: [phone]
Facsimile: 312.377.1184

Hank Bates (State Bar No. 167688)
[e-mail]
Allen Carney
[e-mail]
David Slade
[e-mail]
CARNEY BATES & PULLIAM, PLLC
11311 Arcade Drive
Little Rock, AR 72212
Telephone: [phone]
Facsimile: 501.312.8505

17

Jeremy A. Lieberman
Lesley F. Portnoy
[e-mail]
POMERANTZ, LLP
600 Third Avenue, 20th Floor
New York, New York 10016
Telephone: [phone]
Facsimile: 212.661.8665

Attorneys Plaintiffs and the Proposed Class

12
13
14
15

18

UNITED STATES DISTRICT COURT

19

NORTHERN DISTRICT OF CALIFORNIA

20
21
22

MATTHEW CAMPBELL, MICHAEL
HURLEY, and DAVID SHADPOUR, on
behalf of themselves and all others
similarly situated,

Case No. C 13-5996 PJH
PROOF OF SERVICE BY EMAIL AND
U.S. MAIL

23
Plaintiffs,
24
v.
25
FACEBOOK, INC.,
26
Defendant.
27
28
1215231.1

- 17 -

PROOF OF SERVICE BY EMAIL AND U.S. MAIL
CASE NO. C 13-5996 PJH

1

I am a citizen of the United States and employed in San Francisco County, California. I

2

am over the age of eighteen years and not a party to the within-entitled action. My business

3

address is 275 Battery Street, 29th Floor, San Francisco, California 94111-3339.

4

I am readily familiar with Lieff, Cabraser, Heimann & Bernstein, LLP’s practice for

5

collection and processing of documents for service via email, and that practice is that the

6

documents are attached to an email and sent to the recipient’s email account.

7

I am also readily familiar with this firm’s practice for collection and processing of

8

correspondence for mailing with the United States Postal Service. Following ordinary business

9

practices, the envelope was sealed and placed for collection and mailing on this date, and would,

10

in the ordinary course of business, be deposited with the United States Postal Service on this date.

11

On January 26, 2015, I caused to be served copies of the following documents:

12

1.

PLAINTIFFS’ FIRST SET OF REQUESTS FOR
PRODUCTION OF DOCUMENTS TO DEFENDANT; and
this

2.

PROOF OF SERVICE BY EMAIL AND U.S. MAIL

13
14
15

on the following parties in this action through their respective counsel:

16

Christopher Chorba
Gibson, Dunn & Crutcher LLP
333 South Grand Avenue
Los Angeles, CA 90071-3197
Email: [e-mail]

17
18
19

Joshua Aaron Jessen
Gibson Dunn & Crutcher LLP
3161 Michelson Drive, Suite 1200
Irvine, CA 92612
Email: [e-mail]

20
21
22

Executed on January 26, 2015, at San Francisco, California.
23
/s/ David T. Rudolph
David T. Rudolph

24
25
26
27
28
1215231.1

- 18 -

PROOF OF SERVICE BY EMAIL AND U.S. MAIL
CASE NO. C 13-5996 PJH

Joshua A. Jessen
Direct: +1 949.451.4114
Fax: [phone]
[e-mail]
Client: 30993-00028

April 10, 2015
VIA ELECTRONIC MAIL
Hank Bates, Esq.
Carney Bates & Pulliam, PLLC
2800 Cantrell Road, Suite 510
Little Rock, AR 72202
Re:

Campbell v. Facebook, Inc., N.D. Cal. Case No. 13-cv-05996-PJH

Dear Hank:
Thank you for your letter of April 7, 2015, in which you indicate that Plaintiffs are willing to
“table” or narrow several of their requests for production.
We agree that Plaintiffs’ Request for Production Nos. 12, 15, 18, 23, and 24, which seek
broad categories of documents unrelated to the practice challenged by Plaintiffs in this case,
should be withdrawn.
With respect to Plaintiffs’ proposed narrowed requests (Request for Production Nos. 16, 17,
27, 28, 29, and 30), we are discussing the proposal with our client and will let you know our
position shortly. With respect to Request No. 29, please be advised that there is no specific
list of the “dedicated team of privacy professionals” referenced in the Request, but we have
already agreed to conduct a reasonable search for non-privileged documents sufficient to
identify Facebook’s current and former employees who may possess knowledge relevant to
the practice challenged in this action, and we also have identified witnesses with relevant
knowledge in Facebook’s Initial Disclosures and responses to Plaintiffs’ Interrogatories.
Finally, with respect to the “Relevant Time Period” proposed in your letter (April 1, 2010 to
December 30, 2013), we also are discussing that proposal with our client. To inform our
decision, it would be helpful if Plaintiffs could articulate why they believe they are entitled to
documents after October 2012. Plaintiffs allege in their Complaint that “Facebook ceased
[its] [allegedly] illegal practice at some point after it was exposed in October 2012.” Dkt.
No. 25 ¶ 59 n.3. We understand that Plaintiffs need to confirm the October 2012 date
through discovery (which Facebook will provide), but apart from that confirmation, it is
unclear to us why Plaintiffs need any documents after that time period. Similarly, it would
be helpful if Plaintiffs could articulate why they believe they are entitled to documents before

Hank Bates, Esq.
April 10, 2015
Page 2

the start of the proposed class period (December 30, 2011) and why Plaintiffs propose April
2010 as the start date.
We look forward to discussing these issues with you further next week.
Sincerely,

Joshua A. Jessen

EXHIBIT 33

1
2
3
4
5
6
7

Michael W. Sobol (State Bar No. 194857)
[e-mail]
David T. Rudolph (State Bar No. 233457)
[e-mail]
Melissa Gardner (State Bar No. 289096)
[e-mail]
LIEFF CABRASER HEIMANN & BERNSTEIN, LLP
275 Battery Street, 29th Floor
San Francisco, CA 94111-3339
Telephone: [phone]
Facsimile: 415.956.1008

12

Hank Bates (State Bar No. 167688)
[e-mail]
Allen Carney
[e-mail]
David Slade
[e-mail]
CARNEY BATES & PULLIAM, PLLC
11311 Arcade Drive
Little Rock, AR 72212
Telephone: [phone]
Facsimile: 501.312.8505

13

Attorneys for Plaintiffs and the Proposed Class

8
9
10
11

14
15

UNITED STATES DISTRICT COURT

16

NORTHERN DISTRICT OF CALIFORNIA

17
18
19

MATTHEW CAMPBELL and MICHAEL
HURLEY, on behalf of themselves and all
others similarly situated,
Plaintiff,

20

Case No. C 13-05996 PJH (MEJ)
REPORT OF FERNANDO TORRES IN
SUPPORT OF PLAINTIFFS’ MOTION
FOR CLASS CERTIFICATION

21

v.

Judge: Honorable Phyllis J. Hamilton

22

FACEBOOK, INC.,

HEARING
Date: March 16, 2016
Time: 9:00 a.m.
Place: Courtroom 3, 3rd Floor
|
The Honorable Phyllis J. Hamilton

23
24

Defendant.

25
26
27
28
REPORT OF FERNANDO TORRES IN SUPPORT OF
PLAINTIFFS’ MOTION FOR CLASS CERTIFICATION;
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1
2

I.

Experience and Qualifications
1.

I am a professional economist and have over 30 years’ experience in applied and

3

theoretical economics. In the course of this experience, I have been a consultant, a university

4

professor, and a business manager. Both my undergraduate and post-graduate degrees are in

5

economics, the latter with a concentration in econometrics. Econometrics is the application of

6

mathematics, statistical methods, and computer science to economic data. Since 2004, I have

7

specialized in the analysis and valuation of intellectual property and intangible assets. Currently I

8

am a member and Chief Economist of IPmetrics LLC, an intellectual property consulting firm.

9

2.

During the past ten years, I have undertaken a plurality of valuation engagements

10

where I have appraised the value of a variety of intangible assets in several contexts, such as for

11

licensing and transaction rate setting, for loan collateral analysis, and generally to assist in the

12

decision making process regarding the economic role of intangible assets, including intellectual

13

property. I also regularly give presentations and write about valuation techniques as applicable to

14

intangibles, and have co-designed and taught the course “Valuing Intangible Assets for

15

Litigation” for the National Association of Valuation Analysts.

16

3.

Additionally, I have served as a consultant on numerous cases involving

17

intellectual property infringement contract issues and contractual disputes. I have prepared over

18

50 expert reports and have trial, arbitration, and deposition experience as an expert witness on

19

behalf of both plaintiffs and defendants. I have experience in complex commercial litigation

20

cases nationally. I currently consult with and have consulted with clients in California, New

21

York, Texas, Colorado, and Florida.

22

4.

In the course of my career, I have observed the evolution of online social networks

23

and advertising, both as a business owner and as an economist. In the vast majority of intellectual

24

property infringement cases I have worked on, online advertising and the leverage of information

25

to support such activity play a central role. I have long studied and analyzed how online

26

advertising works as well as the nature of the markets that evolve out of, and are supported by,

27

the internet. Understanding these markets has been enabled not only by my education in

28

economics, but also been informed by my knowledge of programming acquired first in college as
1

REPORT OF FERNANDO TORRES IN SUPPORT OF
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1

a tool for the analysis of economic phenomena, and later in my professional life having developed

2

a financial statement analysis and forecasting software system,1 and an inventory and billing

3

management system for an acute care hospital.2

4

5.

In recent years, I have been called upon to testify in cases where the intersection of

5

social media and advertising has been alleged to have breached rights and principles of privacy,

6

publicity, trademarks, and patents. In some cases, the issues I have reported on for the courts

7

were the benefits derived by the social media/advertising platform infringing the rights of

8

publicity of a class of users,3 while in others the issue has been the economic value of social

9

media marketing in sustaining the viability of traditional media properties.4 Moreover, many

10

trademark infringement and trade secret cases also tend to involve the analysis and assessment of

11

online advertising activity.5

12

6.

I am being compensated for my work in this case at the rate of $375 per hour.

13

Attached hereto as Exhibit A is a copy of my most current curriculum vitae setting forth in detail

14

my qualifications and experience.

15

II.

16

Introduction, Assignment, and Summary of Conclusions
7.

The Plaintiffs’ Consolidated Amended Class Action Complaint (the “CAC”)6

17

alleges that Facebook utilizes information surreptitiously gathered from purportedly “private”

18

correspondence sent between Facebook users, and uses that information in a number of ways,

19

including:

20
21
22
23
24
25
26
27
28

1

The software system was distributed to the nearly 500 nationalized industrial companies in
Mexico to coordinate budgeting and for which I received a Diploma for Public Service from the
Federal Government of Mexico in 1988.
2
Developed for a private hospital in 1991 in Ensenada, Baja California, Mexico.
3
In: Fraley et al. v. Facebook, Inc., case 11-1726 before the USDC for the Northern District of
California.
4
In: S. Mattocks v. Black Entertainment Television, LLC, case 13-61582 before the USDC for the
Southern District of Florida.
5
In, e.g., Gen. C.E. Yeager v. Aviat Aircraft Inc. and S. Horne, case 10-CV-2055 before the
USDC for the Eastern District of California; Laserfiche v. SAP A.G., case 10-7843 (USDC for the
Central District of California); and Estate of Michael Jackson, et al., v. Howard Mann, et al., case
11-cv-584 (USDC for the Central District of California).
6
Consolidated Amended Class Action Complaint, filed April 25, 2014.
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a. to increment the “Like” counts of third party websites that
installed Facebook’s “Like” button social plug-in until, on
information and belief, at least October 2012;7

2
3

b. to catalogue information about specific URLs that were shared
and use that information for targeted advertising or other
purposes;8 and

4
5

c. to catalogue information about Facebook users who shared such
URLs and use that information for targeted advertising or other
purposes.9

6
8.

7

According to the CAC, the putative Class Period began on December 30, 2011.10

8
11

9
9.

10
11

Class:

12

All natural-person Facebook users located within the United States
who have sent, or received from a Facebook user, private messages
that included URLs in their content
, from within two years before the
filing of this action up through the date of class certification.

13
14
10.

15
16

I further understand that the Plaintiffs are seeking certification of the following

In this context, I have been asked to analyze the following questions with regard to

the Class defined above:

17

a. Is there proof common to the proposed Class capable of
showing that—and how much—Facebook profited or otherwise
benefited from the Electronic Communications Privacy Act
(“ECPA”) and the California Invasion of Privacy Act (“CIPA”)
violations alleged in the CAC?

18
19
20

b. Is there a reliable Class-wide or formulaic method capable of
quantifying the amount of such profits or value of such benefits
to Facebook and of allocating those profits to the Class?

21
11.

22

Based upon my work to date, I have reached the following conclusions:12

23
24
25
26
27
28

7

Id. at §§27, 39.
E.g., Id. at §86.
9
E.g., Id. at §30.
10
Id. at §59.
11
Id. at §§27, 39.
12
It is, of course, possible that with additional information, including production from Facebook,
and inputs, these conclusions could be refined. The list of documents I have considered in
forming my opinions is attached to this report as Exhibit B.
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a. There is evidence common to the Class capable of showing that
Facebook profited or otherwise benefited from the scanning
alleged to violate ECPA and CIPA in the CAC. Specifically, as
explained in the body of this report, I have concluded that the
profits or other unjustly-obtained benefits may be analyzed and
quantified based upon Facebook’s records without reference to
individual proof with respect to any member of the Class, such
Class membership being identifiable and ascertainable based
upon Facebook’s records.

2
3
4
5
6

b. Class-wide evidence capable of showing profits or other
benefits to Facebook falls into two categories (1) evidence
concerning Facebook’s use of information derived from private
messages by creating associations within Facebook’s Social
Graph (described in more detail below); and (2) evidence
concerning Facebook’s profits or other benefits resulting from
its campaign to encourage third-party websites (“Marketers”) to
install the Facebook Like button, of which, as alleged,
Facebook’s unlawful scanning was an integral part.

7
8
9
10
11

c. Standard economic methods are capable of reliably quantifying
the aggregate amount of profits to Facebook, and the aggregate
value of other benefits to Facebook that resulted from scanning
and subsequent uses or potential uses of the information derived
therefrom.

12
13
14

d. The damages calculated are based on the economic benefits the
Defendant received from the information intercepted from the
private messages sent by the Class members. Facebook benefits
from advertising revenue from adding the intercepted user-URL
links into their targeting platform and from enhancing their
understanding of how and what users share links to. The
benefit is defined not only by the potential act of generating
additional revenue from targeting ads to the senders of
intercepted messages, but also by the additional use in better
targeting these and similar users (in marketing terms); and the
benefit is ultimately proportional to the number of URLs
intercepted from private messages.

15
16
17
18
19
20
III.

Case Background

21
A.

Facebook, Inc.

12.

Facebook operates the world’s largest social marketing and information platform.

22
23
24
25
26
27
28

The social network side of the business has over 1.5 billion users around the world.13 On August
13

Measured as monthly active users (“MAUs”), which Facebook defines as “a registered
Facebook user who logged in and visited Facebook through our website or a mobile device, used
our Messenger app, or took an action to share content or activity with his or her Facebook friends
or connections via a third-party website or application that is integrated with Facebook, in the last
30 days as of the date of measurement” (Facebook, 2014 10-K Page 35). Current MAUs from:
Facebook, Inc.’s 2015 Q3 Earnings Report (November 4, 2015) Slide 5. At
http://investor.fb.com/results.cfm.
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24, 2015, 1 in 7 people on Earth used Facebook,14 which is equivalent to approximately 51% of

2

all internet users worldwide.15 In the U.S. and Canada, there are currently 217 million (monthly

3

active) users16 which represent 61% of 357 million people in the region.17 Facebook’s advertising

4

network generates revenue in excess of $1.4 billion monthly,18 49.3% of which is attributable to

5

users in the U.S. and Canada.19 Furthermore, Facebook’s most recent disclosure states that, in the

6

U.S. and Canada, Facebook users performed advertising revenue-generating activities at a rate of

7

$9.86 per quarter per user.20

8
9

13.

Facebook’s online social networking service allows users to communicate through

the sharing of text, photograph, video, and internet content. In addition, these activities are

10

supported by a variety of Facebook applications on mobile devices, including Facebook

11

Messenger, Instagram and WhatsApp.21 While Facebook positions its business as focused on

12

“creating value for people, [M]arketers, and developers,” it generates the bulk of its revenues

13

from the latter two categories and then principally to the degree they want to reach the former.

14

14.

Facebook represents a significant opportunity for Marketers due to the

15

combination of the size of the user base and the abundance of rich user data.22 Thus, access to the

16

wealth of information captured on Facebook enables advertisers to reach people across devices

17
18
19
20
21
22
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24
25
26
27
28

14

Facebook CEO Mark Zuckerberg’s public post on Facebook.com of August 27, 2015, at:
(https://www.facebook.com/zuck/posts/10102329188394581).
15
Based on the current estimate of worldwide internet users of 2.919 billion people (14.04 million
in the USA) according to the Wolfram|Alpha Knowledgebase, using data from the World Bank
(http://www.wolframalpha.com/ accessed 10/26/15).
16
Facebook, Inc.’s 2015 Q3 Earnings Report, Slide 5 (op cit.).
17
According to U.S. Census projections (321.37 million people in the USA in July 2015) and
Statistics Canada estimates (35.85 million people in Canada in July 2015) [In:
http://www.census.gov/population/projections/data/national/2014/summarytables.html, and
http://www.statcan.gc.ca/pub/91-002-x/2015002/t002-eng.pdf].
18
Facebook, Inc.’s 2015 Q3 Earnings Report, Slide 8 (op cit.), quarterly data divided by three.
19
Facebook, Inc.’s 2015 Q2 Earnings Report, Slide 10 (op cit.).
20
This is the ratio of quarterly revenue to monthly active users per Facebook, Inc.’s 2015 Q3
Earnings Report, Slide 12 (op. cit.).
21
Facebook, 2014 10-K Page 5 (User and Revenue data cited above do not include Instagram or
WhatsApp users).
22
As expressed by Facebook’s Adam Isserlis, Manager, Corporate Communications,
Ads/Monetization; Colleen Coulter, Product Marketing Communications Manager, in “IAB
Social Media Buyers Guide” available on the Interactive Advertising Bureau website
(http://www.iab.net/socialmediabuyersguide).
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and, importantly, to effectively measure the impact of their advertising. In its public disclosures,

2

Facebook emphasizes that the platform creates value for Marketers by its unique combination of:

3

a. Reach, with over a billion and a half monthly active users in
2015;23

4

b. Relevance, supporting ad targeting by rich demographics and
interests data plus Marketers’ and third party data cross
referencing;24

5
6

c. Social Context, by providing information to leverage
recommendations from friends;25 and,

7
8

d. Engagement, with ad products prompting interaction and
sharing.26

9
10

15.

In this report, I will refer to advertisers that use Facebook’s website and the

11

corresponding development tools to leverage the targeted access to the massive user base as

12

‘Facebook Marketers’ or simply ‘Marketers.’

13

16.

Facebook also represents an important platform for software developers by

14

providing access to a substantial user base, a payment management mechanism, and analytical

15

information about the use of applications.27

16

17.

Facebook has built a dominant position in the social networking market and, as

17

such, attracts a significant amount of consumers’ time and attention. According to the Business

18

Intelligence Report on Social Engagement, in 2013 Americans spent an average of 37 minutes

19

daily on social media, a higher time-spend than any other major internet activity, including

20

email.28 More recently, Facebook claims that “when it comes to time spent by users of the

21

platform, across Facebook, Messenger and Instagram, people are now spending more than 46

22

minutes per day on average.”29 This amount of attention is leveraged by Facebook in providing

23

23

24
25
26
27
28

Facebook, Inc.’s 2015 Q3 Earnings Report, Slide 5 (op cit.). and Facebook, Inc. Form 10K
2012, p. 7.
24
Facebook, Inc. Form 10K 2012, p. 7.
25
Id., p. 8.
26
Id.
27
Facebook for Developers website: https://developers.facebook.com/.
28
Business Insider, Business Intelligence Report on Social Engagement
(http://www.businessinsider.com/social-media-engagement-statistics-2013-12).
29
Mark Zuckerberg’s remarks during the Second Quarter, 2015 Earnings Call (page 1 of the
Footnote continued on next page
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Marketers access to a relevant and sizable audience, and now constitutes the company’s

2

overwhelming source of revenue; currently, advertising accounts for 95.5% of Facebook’s

3

revenue.30

4

18.

From an economic perspective, Facebook is thus a platform business and operates

5

a two-sided market. That is, much like broadcast television and terrestrial radio in the past,31

6

Facebook essentially sells to Marketers access to “the thoughts and emotions” of an audience

7

aggregated on the basis of providing online social media products and user-generated content to

8

“users,” rather than simply the transmission of content. In sharp contrast to broadcast media, with

9

Facebook the access is readily measurable and the advertising messages finely targeted and

10

distributed. Thus, essentially, on one side of the market Facebook accrues users providing online

11

products,32 and on the other it sells advertising placements to Marketers. Furthermore, on the

12

user acquisition side, Facebook competes with other social media offerings, such as Twitter and

13

Google+, and with other online activities (including news and video reading/watching). Further,

14

Facebook is developing the platform as a portal through which users can access news,33 discover

15

content by searching,34 and incorporate more and more online activities.35 On the advertising

16

sales side, Facebook competes with both online advertising outlets, such as Google AdWords and

17

DoubleClick,36 and off-line advertising media (including traditional broadcast TV and print

18

advertising). Facebook’s competitive advantage stems from the power of leveraging the deep

19

Footnote continued from previous page
transcript) held on July 29, 2015. Available at: http://investor.fb.com/results.cfm.
30
Facebook, Inc.’s 2015 Q3 Earnings Report, Slide 8 (op cit.).
31
See, inter alia, Ch. 7-Broadcasting in: H. Vogel, Entertainment Industry Economics,
Cambridge University Press, 8th Ed., 2011.
32
As a company, these products now include Instagram and WhatsApp, expanding the original
Facebook and then Messenger products. Facebook, 2014 10-K, p. 5.
33
For example, with the introduction of the “Instant Articles” initiative and new deals with
publishers like the Washington Post (http://media.fb.com/2015/05/12/instantarticles/).
34
E.g., with expanding the power of Facebook search
(http://newsroom.fb.com/news/2015/10/search-fyi-find-what-the-world-is-saying-with-facebooksearch/).
35
Such as video, with video hosting and action tracking
(http://newsroom.fb.com/news/2015/06/news-feed-fyi-taking-into-account-more-actions-onvideos/), app acquisitions like Instagram and WhatsApp, and with plugins to track activities
outside of Facebook.
36
See Google Products and Advertising Platforms (www.thinkwithgoogle.com/products/).

20
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targeting knowledge available from its unique access to an increasingly complete and

2

computerized social network, including by tracking users beyond the Facebook.com website.

3

Consequently, the two activities, providing online social networking services and selling

4

advertising, are inextricably connected; the profit motive permeates both sides of the operation.

5

19.

Facebook competes for advertising expenditures, among other means, by

6

differentiating its platform from competitors’ as the most effective because of the unique ability

7

to leverage the Social Graph, described in more detail below. Researchers in the field of social

8

and economic networks have noted specifically that they “…find evidence that social advertising

9

is effective, and that this efficacy seems to stem mainly from the ability of targeting based on

10

social networks to uncover similarly responsive consumers.”37 In practice, the superior

11

effectiveness of advertising on this basis is demonstrated by the increasing click-through rates

12

(“CTR”) of ads placed through Facebook as opposed to ads placed through Google’s display

13

network.38

14

B.

The Social Graph

15

20.

The main way in which individual Facebook users knowingly connect with each

16

other is by selecting the “Friend” button to add them to their network. The main way users

17

knowingly interact with brands that have Facebook pages is to select the “Like” button so a

18

“fan”39 link is created allowing the Facebook page’s posts to appear on each fan’s home page (on

19

the “news stream” of posts from friends and liked pages). Facebook also creates connections that

20

users may not be aware of. For example, beyond the Facebook.com website or applications, users’

21

browsing and other activities are also able to be logged using cookies,40 pixels41 and similar

22
23
24
25
26
27
28

37

C. Tucker, “Social Advertising,” February 15, 2012, SSRN (http://ssrn.com/abstract=1975897).
Since mid-2014 Facebook CTRs have increased by 35% vis-à-vis a 25% increase on Google’s
network, according to the latest “Digital Advertising Report Q3 2015”, Adobe Digital Index
(www.cmo.com/adobe-digital-index.html), p.18.
39
In Facebook marketing, while it is natural to speak of a “Friend” of a person, the equivalent for
brands is to use “Fan” instead, although they may also be used interchangeably.
40
Cookies are small files that are stored on the user’s device by the website or application being
used and some ads being viewed.
41
Pixel tags in this context are also called clear GIFs, web beacons, or pixels and are small blocks
of code on a webpage or application that allow them to perform actions such as read and place
cookies and transmit information to Facebook or its partners.
38

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internet technologies.42 The resulting information is used in delivering targeted advertising and

2

refining the information represented on the Social Graph.

3

21.

Facebook’s Social Graph represents the integration of information collected by

4

Facebook about Facebook users, and encompasses their location, demographics, interests,

5

behaviors, and connections, in order to target advertising and marketing communications to

6

specific groups of users identified by these attributes.43

7

22.

Figure 1 illustrates one hypothetical user on the social network (at the center),

8

technically referred to as a “node.” This user is connected to two friends by lines called “edges,”

9

has “Liked” a page (For the F8 Developers Conference, illustrated by its logo on the upper right

10

corner), is interested in cooking (link labeled “cook”), has watched a video on Netflix (bottom

11

right link), and has listened to music on Spotify (middle left link).

12
Figure 1
Facebook Social Graph Illustration44

13
14
15
16
17
18
19
20
21
22
23
24
42

25
26
27
28

See also https://www.facebook.com/help/cookies/.
Although the term is borrowed from Mathematics and Sociology, it was introduced in the
Facebook context by Mark Zuckerberg during the 2007 F8 Developers Conference on May 24,
2007.
44
From Business Insider
(http://static3.businessinsider.com/image/4f5112e169bedd1526000061/facebook-opengraph.jpg).
43

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23.

Figure 1 is a partial visual representation of the Social Graph. In practice, the

2

information contained in the Social Graph is stored in a (complex and distributed) database. The

3

data model Facebook utilizes is called TAO (The Objects and Associations).45 The constituent

4

parts of this model – illustrated above – are Objects (representing the “nodes,” or data items, such

5

as a user or a location) and Associations (representing the “edges,” or relationships between

6

Objects).

7

24.

Thus, as illustrated, even activities (accessing pages, clicking on Like or Share

8

buttons) performed on websites or applications outside of Facebook can, and are, represented in

9

the Social Graph. Granting controlled access and writing abilities to this wealth of information to

10

registered developers, on April 21, 2010, Facebook released the Open Graph Protocol,46 which

11

enables any web page to become a rich object in a Social Graph, and the Graph API,47 which is

12

the primary way for apps to read and write to the Facebook Social Graph.48 Facebook builds and

13

maintains full access to the full Social Graph leveraging its own record of users’ connections

14

behind-the-scenes.

15

C.

The Like Button

16

25.

Facebook social plugins, such as the “Like” Button, are lines of code that third-

17

party websites can integrate into their sites, which display a Facebook logo and execute the

18

programmed code when the page is accessed and/or a Facebook user clicks on it.49 Facebook first

19

implemented the Like Button in or around February 200950 and, in Facebook’s F8 conference in

20
21
22
23
24
25
26
27
28

45

See https://www.facebook.com/notes/facebook-engineering/tao-the-power-of-thegraph/10151525983993920
46
The Open Graph protocol is programming code used on Facebook to allow any web page to
have the same functionality as any other object on Facebook. See Open Graph Protocol open
source website (http://ogp.me/).
47
API, or “Application Programming Interface,” is the code that a third party may utilize to build
software on top of Facebook’s platform. Through Facebook’s API, the third party product is able
to utilize parts of Facebook’s code (and access certain tranches of Facebook’s data) for its
functionality.
48
See https://developers.facebook.com/docs/graph-api.
49
Facebook SDK Documentation
(https://developers.facebook.com/docs/javascript/quickstart/v2.5#plugins).
50
J. Kincaid in: TechCrunch (http://techcrunch.com/2009/02/09/facebook-activates-like-buttonfriendfeed-tires-of-sincere-flattery/).
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2010, it was opened up for third party developers for marketing and application development

2

uses.51

3

26.

As illustrated in Figure 1 above, a Like becomes a Social Graph connection

4

between a user and a Marketer that has installed a Facebook Social plug-in.52 Generally speaking,

5

“Liking” a “Page” means the user is connecting to that Page, and “Liking” in reference to a post

6

from a friend, which means the user is letting that friend (or friend of a friend) know that the user

7

“likes” his or her post (without leaving an explicit comment).53 The first is a link between a user

8

and a Marketer, the second is a link among users. The “Likes” recorded as a result of scanning

9

private messages addressed in this case are of the first type.

10

27.

Facebook developed social plug-ins, such as the “Like” button to continue

11

expanding its network by affiliating with Marketers or third party websites. Social plug-ins

12

enable advertisers and Marketers to integrate user activity inside and outside of the Facebook

13

website. The initial performance metric for these advertising activities was the number of

14

“Likes” associated with a company within Facebook and, increasingly, outside of Facebook on

15

Marketers’ websites.

16
17

28.

Figure 2 below is an illustration from Facebook materials addressed to Marketers

on the benefits of using social plugins.

18
19
20
21
22
23
24
25
26
51

27
28

Facebook F8 April 21, 2010.
Facebook, Social Plugins FAQs, at: https://developers.facebook.com/docs/plugins/faqs/#ref.
53
See Facebook Help Center at: https://www.facebook.com/help/228578620490361.
52

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2
3
4
5
6
7
8
9
10
11

29.

Facebook is well aware of the power of the Like button to generate actionable

signals for advertisers.54 From its launch in April 2010,

12

.55

13

30.

Facebook has promoted this social plug-in aggressively to third-party websites by,

14

for instance, taking control of News Feed content.56 In turn, Marketers that wished to maintain

15

their reach via the social network had to respond by increasing the integration of Facebook into

16

their marketing strategies and budgets.57

17

D.

The Alleged Violations

18

31.

Facebook published a privacy policy and posted descriptions of Facebook’s

19

private messaging service claiming it would provide a way to communicate privately and that the

20

messages would be private.58

21
22

54

According to internal communications produced in discovery, for example, Facebook personnel
sought

23
24
25
26
27
28

(FB000011746).
55
According to Defendant’s internal communications
(FB000011715-6).
56
See Facebook Media, “An Update to News Feed: What it Means for Businesses”
(https://www.facebook.com/business/news/update-to-facebook-news-feed) and “News Feed FYI:
Balancing Content from Friends and Pages” (http://media.fb.com/2015/04/21/news-feed-fyibalancing-content-from-friends-and-pages/).
57
See, e.g., MarketingLand (http://marketingland.com/facebooks-latest-tweaks-favor-friendscould-hurt-page-reach-125931).
58
CAC, at §§21-24.
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32.

The CAC alleges that Facebook actually scanned the content of private messages

2

and used information concerning any URLs contained within the messages to artificially increase

3

the appearance of user engagement with third-party websites by increasing the count on such

4

sites’ Like buttons, as well as for other, undisclosed, purposes.59

5

33.

Additionally,

6
7
.60

8
9

34.

Consequently, in the context addressed in the background section, the following

10

methodological discussion addresses two distinct aspects of how Facebook benefited from the

11

accused actions:

12

a. Benefits from the additional information that enhances the
Social Graph as a means to increase advertising revenue and
profits; and,

13
14

b. Benefits from artificially increasing the “Like Count” on third
party websites using Facebook social plugins,61 because it
enhances clients’ impression of how effective Facebook
Marketing is and incentivizes Marketers’ willingness to invest
in Facebook Marketing.

15
16
17
18
19
20
21
22
23
24
25
26
27
28

59

URL stands for Uniform Resource Locator, the unique identifier of each document on the
internet. Defined initially by Tim Berners-Lee in: “Uniform Resource Locators (URL): A Syntax
for the Expression of Access Information of Objects on the Network” (March 1994) in:
http://www.w3.org/Addressing/URL/url-spec.txt.
60
CAC at §§25-26.
61
At least up to the end of 2012.
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IV.

The Measure of Damages

2
A.

Benefits Resulting from Enhancing the Social Graph by Incorporating
Intercepted Data.

35.

As discussed below, the incremental value of Facebook’s benefits from enhancing

3
4
5

the Social Graph by including data intercepted in private messages can be calculated on a per

6

URL link basis. This incremental profit from Facebook’s accused behavior can be calculated by

7

utilizing the corresponding inputs and the algorithm discussed in this section.

8
9

36.

It is not disputed that Facebook’s Social Graph is a valuable asset. The value

fundamentally arises from the aggregation of the collected information from all users in general,

10

as well as from the information intercepted from the Class members’ private messages. By its

11

actions, Facebook has denied Class members the ability to restrict access to elements of

12

information about them (URL links) and is profitably utilizing the information by enhancing the

13

value of its own social media advertising platform, which helps Facebook maintain and grow its

14

market share in the face of competition. Thus, by gathering data from Class members as alleged

15

by Plaintiffs, Facebook directly benefits by enhancing the informational content and targeting

16

power of their key revenue-generating asset: the Social Graph.

17

37.

The more nuanced the data and the inferences that can be drawn from it, the more

18

effective Facebook marketing becomes and the greater the share of advertising revenue that the

19

Company can extract. For example, in a recent Earnings Call Facebook’s Chief Operating

20

Officer, Sheryl Sandberg, highlighted an advertising campaign on Facebook in which the fast

21

food chain Wendy’s wanted to reach a very specific target group for the launch of a new product

22

(“Jalapeño Fresco Spicy Chicken”): “millennials that are spicy food lovers”. Wendy’s worked

23

with Facebook to create a campaign with five video ads specifically targeted at Facebook users

24

that fit that socio-demographic (millennials) and affinities (spicy food lovers) profile. The

25

targeting of the campaign, based on the information in the Social Graph, was successful in

26

exceeding goals in terms of: (a) the impact of the ads, as significantly more consumers recalled

27

seeing the ads; and (b) in terms of sales, with a significant increase in purchases among the target

28
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segment.62 The more precise the socio-demographic and affinities profile, the more successful

2

and, therefore, profitable, an advertising campaign can be. The value of the Social Graph asset is

3

significant. Working off of publicly-available information, this value can be ascertained as

4

follows, applying the generally recognized Income Approach to Valuation.63

5

38.

Under the Income Approach, the value of an asset is measured by the net present

6

value of the net economic benefit to be received over its economically useful life.64 The three

7

essential factors of this measurement of value are: (1) the value of the net income stream

8

(revenue minus expenses) that can be generated by the asset; (2) an assumption as to the duration

9

of the net income stream; and (3) an assumption as to the risk associated with the realization of

10

the anticipated net income.65 These factors can be determined mainly based on Facebook’s

11

financials.

12

39.

Focusing on the Social Graph delimited as far as possible to the U.S., Facebook

13

has stated that, as of June 30, 2015, advertising revenue from the U.S. is in the order of $1,593

14

million per quarter.66 This is revenue attributable to the Social Graph because it enables the

15

unique selling proposition of targeted advertising on Facebook. Furthermore, according to

16

Facebook, the average cost of revenue, marketing and sales, and general and administrative

17

expenses during the same period was 40.75% as a percentage of revenue.67 Thus, a profit of

18

$3,776 million per year is attributable to the U.S. portion of the Social Graph asset.68

19

62

20
21
22
23
24
25
26
27
28

Example discussed by Sheryl Sandberg (Facebook COO) during the 2015 Q2 earnings call held
on July 29, 2015. Available at: http://investor.fb.com/results.cfm.
63
See, inter alia, G.V. Smith and R.L. Parr, Valuation of Intellectual Property and Intangible
Assets, John Wiley & Sons, 2000; R. F. Reilly and R.P. Schweihs, Valuing Intangible Assets,
McGraw Hill, 1999.
64
See, e.g.: Smith and Parr (2000), p. 164.
65
Ibid, p. 169.
66
This is the average of the quarterly advertising revenue from the activities of users located in
the U.S. and Canada during the four quarters between April 2014 through June 2015, $1,771
million, as disclosed in: Facebook, Inc.’s 2015 Q2 Earnings Report (July 29, 2015) Slide 9 (op
cit.). A further adjustment is made to exclude data for Canada, multiplying by the ratio of the size
of the U.S. Population to the total of the two countries (89.96% = 321.37 / (321.37+35.85) per
official U.S. Census and Statistics Canada sources (op. cit.).
67
Facebook, Inc.’s 2015 Q2 Earnings Report (July 29, 2015) Slide 13 (op cit.). Per accepted
valuation standards, Research and Development expenses are not includable in this valuation
because, by definition, their effects are in the future, not as of the valuation date (June 30, 2015).
68
The result of multiplying the quarterly revenue times four quarters and deducting 40.75% for
Footnote continued on next page
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40.

The economically useful life of the asset in question, that is, the usefulness of the

2

information represented in the Social Graph, is not immutable; people’s locations, friends,

3

affinities, and interests change over time. While the Social Graph contains a varied spectrum of

4

information, as a proxy for the likely obsolescence of the information embodied in the Social

5

Graph, the most significant indicator, in my opinion, is geographical mobility. One of the

6

primary selection criteria in defining a target market is location; there is generally no point in

7

advertising to users in locations where sales cannot be made, while other primary attributes tend

8

not to change as often.69

9

41.

Geographical mobility is periodically measured by the U.S. Census. On average,

10

in the span of five years, 35.4% of the population moves.70 This represents an exponential

11

decline in the accuracy of address information of 8.37% per year.71 At this rate, 50% of people

12

will have moved in about eight years.72 In addition, considering the broader context of the

13

valuation of comparable intangible assets for financial reporting, a marketing asset frequently

14

identified in business mergers and acquisitions is the Customer List. The median remaining

15

economic life of Customer Lists among publicly traded U.S. companies is also eight years.73

16

Thus, while it is likely that a lot of the information on the Social Graph will still be current after

17

eight years, a primary attribute and targeting selector (location) will not be accurate for the

18

majority of people. Based on these considerations, I have concluded that a reasonably reliable

19

remaining useful life for valuing the Social Graph asset is eight years.74

20

Footnote continued from previous page
expenses.
69
These would be parameters such as age, gender, household income, which change predictably,
slowly, sporadically, or not at all.
70
U.S. Census Bureau, Geographical Mobility: 2005 to 2010 (December 2012), Table 2, Page 5
(http://www.census.gov/prod/2012pubs/p20-567.pdf).
71
This equivalent annual rate is calculated algebraically solving the equation expressing the
Census fact that the ratio of the population in year 5 relative to the population in year 0 is 64.6%
(100% - 35.4%) and this is equal to (1 + annual rate)5.
72
Technically, in 7.9 years, calculating: log(0.50) / log(1–0.0837).
73
Data from: Business Valuation Resources, “Benchmarking Identifiable Intangibles and Their
Useful Lives in Business Combinations” BVR 2012, p. 66 (www.bvresources.com).
74
This is a conservative position since, in reality, Facebook users tend to maintain their
information current as part of the normal use of the network. The asset is being valued “as is” in
mid-2015, without considering continued updating.

21
22
23
24
25
26
27
28

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42.

A reasonable estimate of the corresponding market discount rate for this asset can

2

be based on the most current assessment of the risk factors recommended by the most reputable

3

industry sources.75 The discount rate is made up of a series of components reflecting the time-

4

value of money (the so-called Risk Free rate76), the general additional risk of equity returns

5

(known as the Equity Risk Premium77), the additional variations of net income in the relevant

6

industry (the Industry Risk Premium), and the incremental risks unique to the asset class. Thus I

7

considered the risk-free rate of 4.0%,78 a market equity risk premium of 5.0%,79 as well as an

8

advertising industry risk premium of 3.66% based on generally accepted data sources.80 In

9

addition, I considered a risk premium reflecting the incremental risks associated with intangible

10

assets relative to financial and tangible business assets of 6.0%.81 Adding together these various

11

components, I thus arrived at the discount rate for the Social Graph asset of 18.66%.82

12

43.

Consequently, applying the aforementioned method and inputs, which are the type

13

of methods and parameters applied by valuation professionals like myself, the (U.S.) Social

14

Graph asset relating to the U.S.is valued at approximately $15 billion, as illustrated in the

15

following table:

16
75

17
18
19
20
21
22
23
24
25
26
27
28

Duff & Phelps, 2015 Valuation Handbook: Guide to the Cost of Capital, John Wiley & Sons,
2015
76
In valuation theory, this rate is the return available on a security that the market generally
regards as free of the risk of default. In practice, in the U.S., this is the yield on government
securities, adjusted (or normalized) to remove the distortion of the artificially depressed,
unsustainable rates during the 2008 financial crisis. [Duff & Phelps (2015), Ch. 3].
77
Conceptually, this premium is defined as the extra return, over the expected yield of risk-free
securities, which investors expect to receive from an investment in the market portfolio of
common stocks (Duff & Phelps 2015, pp. 3-17).
78
Technically, this rate is the normalized 20-year U.S. Treasury yield [Duff & Phelps (2015),
Ch. 3].
79
This is the considered forward equity risk premium recommended by Duff & Phelps.
80
See, Duff & Phelps (2015), pp 3-35 and 5-21 (The industry risk premium corresponds to a Beta
of 1.73). In addition, some valuation models consider a specific “Size Premium” which, in this
case, is not necessary since the Facebook Social Graph is evidently the largest marketing database
in the economy.
81
As recommended by IPmetrics for intellectual property (IP) valuation analyses based on market
interest rate spreads for IP-backed securities (See, e.g., M. Loumioti, “The use of intangible assets
as loan collateral” Harvard Business School, 2011 Available at the Social Science Research
Network: http://ssrn.com/abstract=1748675).
82
This is the result of adding the risk-free rate and the three identified risk premiums
corresponding to equity, industry, and asset considerations (18.66 = 4 + 5 + 3.66 + 6).
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Table 1

U.S. Social Graph Valuation
2

(As of 2015 Q2)

3
Year

4

Annual
Profit
($ millions)

Discount
Factor
(at 18.66%)

Discounted
Value
($ millions)

1

0.71022

2,682

3,776

0.59853

2,260

3,776

0.50441

1,905

3,776

0.42509

1,605

6

3,776

0.35824

1,353

7

3,776

0.30190

1,140

8

9

3,776

5

8

$ 3,182

4

7

0.84274

3

6

$ 3,776

2

5

3,776

0.25443

961

Total Value:

10

$ 15,087

11
44.

12

Since Facebook already has the infrastructure and software development platform

13

in place to develop and grow the Social Graph, as well as access to the marketing clients that fund

14

the advertising campaigns, the additional information collected through the accused activities has

15

arguably zero incremental cost. Therefore, from an economic perspective, virtually all of the

16

incremental advertising revenue generated from the enhancement can justifiably be considered

17

incremental profit to Facebook. Therefore, the impact of additional information intercepted from

18

private messages on Facebook’s revenue flows directly to the bottom line (profits).
45.

19

With the relevant quantitative information, I would estimate the value of the

20

enhancement to the Social Graph as commensurate with the ratio of (1) intercepted URLs in

21

private messages during the Class period to (2) the total number of links on the Social Graph.
46.

22

Absent specific Facebook network data,83 from public information it can be

23

ascertained that during 2010, Facebook had an average of 127.1 million monthly active users in

24

the U.S.84 On average, within Facebook as a whole, the average monthly active user sent nearly

25

83

26
27
28

84

According to Facebook Inc.’s Form 10-K Disclosures, The four quarters of 2010 in the U.S. &
Canada had MAUs of 130,137,144, and 154 million respectively. The average cited is adjusted to
exclude users in Canada.
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43 messages per month.85 Thus, in 2010, I estimate that the U.S. user base sent approximately

2

65.4 billion messages.86 The following Table shows the results of these estimates on an annual

3

basis.

4

Table 2

U.S. Messaging Activity

5

(2010 – 2015)

6

Monthly
Average
Users

Estimated
Messages

(millions)

(millions)

2010

127

65,353

2011

155

79,464

10

2012

169

86,867

11

2013

178

91,725

2014

184

94,848

2015H1

190

97,855

7

Year

8
9

12
13
14

47.

Since user engagement has increased over the Class Period,87 the estimates on

Table 2 may well understate the amount of messaging activity on the network.

15

48.

The relative impact of this additional, but allegedly wrongfully obtained

16

information, on the value of the Social Graph can in principle be ascertained as the addition of

17

information to the Social Graph. In the absence of detailed information about it, I have relied on

18

public information to approximate the optimal analysis.

19
20

49.

Facebook researchers have published results of the formal characterization of the

entire social network of active members88 of Facebook in May 2011, comprising 721 million

21
85

22
23
24
25
26
27
28

Considering Facebook’s disclosure in connection with the redesign of the Messenger platform,
stating that 350 million MAUs sent 15 billion messages per month, or an average of 42.857
messages per MAU/month, at: https://www.facebook.com/notes/facebook-engineering/theunderlying-technology-of-messages/454991608919.
86
This is the result of multiplying 42.857 messages/user/month times the 127.07 million users,
times 12 months.
87
According to Facebook, between August 2012 and May 2013 user engagement, as illustrated in
the number of likes generated per day, increased from 2.7 Billion to 4.5 billion on average
(https://www.facebook.com/photo.php?fbid=10151908376831729&set=a.10151908376636729.1
073741825.20531316728&type=1&theater).
88
Defined for analysis as “the number of members that logged into the site in the 28 days before
the May 2011 date of the study and had, at least, one Facebook friend.” See, e.g.: J. Ugander, B.
Karrer, L. Backstrom, C. Marlow, “The Anatomy of the Facebook Social Graph”, White Paper,
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active users.89 From this universe, 149 million are U.S. Facebook users.90 Among these U.S.

2

social network users, there were 15.9 billion friendship links or graph “edges,” and the average

3

U.S. user had around 214 Facebook friends.91 The graph is highly connected, in the sense that

4

typical Facebook members are linked (as “friends” and “friends of friends”) in such a way with

5

the rest of the network as to be able to reach the vast majority of individuals with only a few

6

“hops” or jumps from one friend to another. Specifically, in the U.S. network the average

7

distance between people was found to be 4.3 friends and, furthermore, 96% of all Facebook

8

members were within 5 degrees of separation.92

9

50.

This high degree of “connectedness” is one aspect of the Social Graph that makes

10

it attractive for advertisers and why recommendations from Facebook Friends can be so effective;

11

properly targeted, relatively few recommendations can reach virtually the whole potential market.

12

Moreover, with interests, brand pages, and other actions, the Social Graph now includes more

13

data points (“nodes”) and links (“edges”) than just Facebook Friends. It is the targeting, and

14

specifically the granularity and breath of the targeting information that is enhanced by additional

15

user–URL links, which Facebook gathered unlawfully from intercepting and scanning private

16

messages.

17

51.

Therefore, the economic value of the benefits Facebook derives from the

18

unlawfully gathered user–URL links is proportional to the impact of this additional information

19

on the total information on the Social Graph. In principle, the benefit to Facebook in this respect

20

would be measured by attributing the corresponding portion of the incremental value of the Social

21

Graph to the accretion of the unlawfully gathered links.

22

52.

In other words, at a point in time (t), the value of the Social Graph to Facebook can

23

be expressed as the product of the number of links (L) in the Graph times the value, or worth, of a

24

link (w):

25

Footnote continued from previous page
18 Nov. 2011, Cornell University (http://arxiv.org/abs/1111.4503v1), p. 2.
89
Id. at p. 14.
90
This is nearly 60% of the eligible U.S. population at the time, see Ugander, et al. (2011) p. 2.
91
Ugander, et al. (2011) p. 2.
92
Ugander, et al. (2011) p. 5.

26
27
28

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Vt = Lt × wt

1
2

At the next period (t+1), the value is:
Vt+1 = Lt+1 × wt+1

3
4

The change in value to Facebook, the incremental benefit, is then:
ΔV = Vt+1 – Vt = Lt+1 × wt+1 – Lt × wt .

5
6
7

53.

Adding and subtracting the value of today’s links at yesterday’s unit value (Lt+1 ×

wt):

8

ΔV = Lt+1 × wt+1 – Lt × wt + Lt+1 × wt – Lt+1 × wt

9

and re-grouping the components of this equation, we have:
ΔV = Lt+1 (wt+1 – wt ) + (Lt+1 – Lt ) wt

10
11

54.

Thus, this equation can be interpreted as stating that: The incremental benefit to

12

Facebook is the sum of the effect of the change in the value of a link, plus the effect of the change

13

in the number of links. Only the second component is directly attributable the capture of

14

additional links, so that the measure of damages (D), with full information, would be calculated

15

as follows, considering only the unlawfully gathered additional links:

16
17
18
19

D = (Lt+1 – Lt ) wt
55.

The calculation of the total value is straightforward; multiplying the corresponding

link value to obtain the incremental benefit to Facebook.
56.

The economic benefit to Facebook from the intercepted links can then be

20

estimated applying the per link values, i.e. wt, to the incremental number of links attributable to

21

the intercepted messages, i.e. (Lt+1 – Lt ).

22
23
24

57.

With the input of the number of intercepted URLs, this value per link estimate can

be applied to determine the total benefit to the defendant.
58.

All Class members are subject to the accused scanning and, in this sense, are

25

injured in the same manner, while Facebook benefits from the aggregate information intercepted

26

out of all the messages.

27
28

59.

Facebook benefits from advertising revenue from adding the user-URL links into

their targeting platform and from enhancing their understanding of how and what users share
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links to. The benefit is defined not only by the potential act of generating additional revenue

2

from targeting ads to the senders of intercepted messages, but also by the additional use in better

3

targeting these and similar users (in marketing terms); and the benefit is ultimately proportional to

4

the amount of information intercepted from private messages.

5

60.

Therefore, it is my opinion that a proper attribution of damages among Plaintiff

6

Class Members, calculated as benefits received by the Defendant, should be based on the number

7

of links (URLs) intercepted.

8

B.

Benefits from Inflating the Like Count on Third Party Websites

9

61.

According to the CAC, Facebook also benefits from using the information

10

obtained from the intercepted messages by increasing the counter associated with the “Like”

11

button on third party websites.93 Independently of the actual advertising revenue as analyzed in

12

the previous section, Facebook benefits by providing additional perceived value to all Marketers

13

using these counters to evaluate the effectiveness of Facebook marketing. Due to the wrongful

14

capture of links, and exacerbated by the double counting, Facebook marketing appeared more

15

effective to Marketers and, in turn, Facebook’s clients were induced to extend their relationship

16

with Facebook, not simply by increasing advertising budgets, but at least in part by investing

17

more in building Facebook Pages and installing a variety of plugins feeding additional

18

information for Facebook’s targeting and marketing purposes.

19

62.

As explained in this section, the economic benefit derived by Facebook

20

attributable to one specific way in which it has used the information obtained from the Class

21

Members messages to increase the “Like” count on its clients’ websites lies between two bounds:

22

a higher bound represented by the cost that client websites saved by not having to acquire

23

additional “Likes” calculated at a dollar amount “Y” per “Like”; and a lower bound determined

24

by the market value of artificially acquired “Likes” for pages made possible by manipulating the

25

counting system, of a different dollar amount “Z” per “Like.” This amount represents a cost

26

savings or benefit Facebook was able to provide to its clients directly as a result of the breach of

27

privacy of messages and identifying URLs of Facebook Marketers. Facebook thus benefits from

28

93

CAC at §27 and 39.
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the higher usage rates from Marketers incentivized by the higher Return of Investment (ROI) of

2

the advertising expenditures through the Facebook platform.

3

63.

Marketers are interested in increasing the number of “Likes” associated with their

4

use of the social plugins on their websites outside of Facebook, not simply in growing the number

5

of “Likes” on their Facebook pages.

6

64.

The importance of Marketers’ website counters being affected by the alleged

7

unlawful actions in this case resides in the fact that, during the Class period, it was a key

8

performance indicator of the marketing function for Facebook’s clients: the Marketers or

9

advertisers on whose websites it was shown. Advertisers, as businesses, are interested in the

10

return on their expenditures in advertising; the conventional ROI which compares gains from

11

advertisements with their cost. While the cost is relatively straightforward to ascertain, in the

12

digital advertising environment, gains from advertising are susceptible to estimation in a variety

13

of ways, such as by the number of visitors to a web page, the number of incoming links, the

14

activity on social networks (e.g., followers, comments, “retweets” or “shares,” references in

15

relevant blogs, views on social media web sites, RSS feed subscribers, among others).94 In the

16

Facebook environment, the number of Likes measured is typically interpreted as an indicator of

17

the reach of an advertising strategy and, given the particular brand/product combination, as a

18

factor in generating sales.95

19

65.

For this analysis, the general principles applied in identifying market valuations of

20

the economic worth of “acquiring” or “attracting” Facebook users to express their affinity for a

21

brand are consistent with the general Cost Approach to valuation; the measurement of value by

22

reference to the amount of money that would be required to replace the functionality of the

23
24
25
26
27
28

94

See, for example, Perdue, D. J. (2010). Social media marketing: Gaining a competitive
advantage by reaching the masses. Social Media Marketing, pp. 1, 3–36.
95
By definition, Sales can be seen as the product of marketing reach, times the impact of the ad
(leads per ad), times the yield (sales per lead). Thus, with a given degree of impact and yield, a
higher reach, measured by the Like count for example, generates higher sales. See, e.g.: D.
Buhalis and E. Mamalakis, “Social Media Return on Investment and Performance Evaluation in
the Hotel Industry Context,” in: I. Tussyadiah, A. Inversini (eds.), Information and
Communication Technologies in Tourism 2015, DOI 10.1007/978-3-319-14343-9_18, pp. 241253.
23

REPORT OF FERNANDO TORRES IN SUPPORT OF
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1

subject asset (the Like).96 Ultimately, the realized value of a specific set of “Likes” would

2

generally exceed the cost, to a degree depending on the effectiveness of the specific marketing

3

strategies implemented to leverage them in practice.

4

66.

The effectiveness of the then-novel social network advertising campaigns was

5

typically measured by the number of Likes.97 Knowledge of the mechanics of this “Like” counter

6

obviously led to manipulations, such the “purchase” of spurious “likes,”98 which, at least in one

7

instance, had a market value as low as $0.075 per “like” and even deceptive campaigns that

8

encouraged people to copy and paste in their public Facebook posts certain texts with the

9

appropriate URLs embedded in them, so the Facebook mechanism would reward the intended

10

website with a viral increase of “Likes.”99

11

67.

Ultimately, the meaning of the counter became so diluted by 2013 that both

12

analytics firms and Facebook changed their assessment of the counter as well as the need for the

13

button graphic, developing the Facebook pixel and other hidden plug-ins, and began

14

supplementing these performance measures with other factors.100

15
16
17
18
19
20
21
22
23
24
25
26
27
28

96

The underlying assumption is that the price of new assets (i.e., Likes) is commensurate with the
economic value of service that the property can provide during its life. See: G.V. Smith and R.L.
Parr, Valuation of Intellectual Property and Intangible Assets, John Wiley & Sons, 2000, p. 164.
97
Advertising generally strives for the general notion of Reach (“the number or percentage of
target audience members exposed at least once to media carrying an advertising message”). In
the online environment, user activity can be measured in great detail and the number of clicks on
a specifically-designed button, or other specific user action (including a link or URL), as reflected
in the Like count provide that measurement.
98
See, e.g., National Public Radio, Planet Money “For $75, This Guy Will Sell You 1,000
Facebook 'Likes'” originally broadcast on May 16,
2012(http://www.npr.org/sections/money/2012/05/16/152736671/this-guy-will-sell-you-sell-you1-000-facebook-likes).
99
Some hoaxes that repeatedly play out in the Facebook context are similar to a “chain letter”
model where users are encouraged to “copy and post” texts such as bogus “copyright”
notifications and spurious claims of privacy claims based on international law. See, e.g., W.
Oremus, “That Facebook Copyright Notice Is Still a Hoax” November 26, 2012, Slate
(http://www.slate.com/blogs/future_tense/2012/11/26/facebook_copyright_notice_berner_conven
tion_status_update_still_a_hoax.html).
100
Nielsen, the company behind the Ratings system, now emphasizes the notion of ‘Brand Lift’ to
measure the effectiveness of online marketing and, specifically, through Facebook (Nielsen
“Quickly and Accurately Measure the Effectiveness of Your Online Ad Campaigns” available as:
www.nielsen.com/content/dam/nielsen/en_us/documents/pdf/Fact%20Sheets/Nielsen%20BrandL
ift.pdf).
24

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1

68.

Therefore, Facebook benefited from the accused practice of using the results of

2

scanning supposedly private messages for URLs and affecting Like counts because this practice

3

gave its clients, Marketers, an incremental impression of effectiveness of their Facebook

4

marketing campaigns. Marketers perceiving an incremental return of their spending on Facebook

5

campaigns were undoubtedly encouraged to allocate additional funds to these campaigns.

6

69.

Due to the success of social online networking, acquiring Likes on Facebook pages

7

and outside websites has become a fundamental goal for brands in all Business-to-Consumer

8

markets over the past decade. In studies aimed at estimating the costs of acquiring fans,

9

advertising industry experts have based their analysis on the average of paid advertising needed,

10

on average, to acquire a Facebook page “Like” and convert them into paying customers. In 2011,

11

a study quoted in the well-known trade publication Advertising Age,101 considered 5 million

12

Facebook ads placed by over 50 companies, the acquisition cost of “Fans,”102 calculated by

13

dividing the total cost of clicks by the total number of actions, was found to be $9.56 less than the

14

cost to acquire the same level of sales from non-Fans.103 This is an average of the sampled

15

companies from mostly the consumer packaged goods, auto and finance. Necessarily, the cost

16

per acquisition varies by industry, by product, as well as by the desired behavior from potential

17

customers when visiting the Facebook page. Table 3 shows the average effect summarizing the

18

findings, comparing the cost of attracting a variety of actions (called “conversion” events)

19

between Facebook users that previously “Liked” the corresponding brand, i.e., Fans, and visitors

20

that had not, i.e., Non-Fans.

21
22
23
24
101

25
26
27
28

Advertising Age, Nov. 22, 2011.
“Fans” standing for Facebook Friends on Brand Pages, is the term typically used in advertising
industry. See, inter alia, Peter Elbaor, “The Interconnection of Facebook Fan Pages” October 28,
2011, ComScore Insights Blog, (http://www.comscore.com/Insights/Blog/The-Interconnectionof-Facebook-Fan-Pages).
103
Study by SocialCode, LLC reported in trade publication Advertising Age
(adage.com/print/231128).
102

25

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1

Table 3
Cost per Acquisition (CPA) on Facebook
Source: SocialCode, LLC
(May-Sept 2011)

2
3

Conversion Type

4

Non-Fan
CPA

Fan
CPA

Difference

App Install

5

$8.49

$ 2.61

$5.88

Contest Submission

76.25

17.21

59.04

6

Contest Voting

21.09

3.26

17.83

7

Fan Acquisition

5.17

3.39

1.78

Program Sign-Up

75.90

41.25

34.65

Purchase

43.86

12.88

30.98

Sweepstakes Entry

5.81

2.57

3.24

$ 14.93

$ 5.37

$9.56

8
9

TOTAL

10
11

70.

Since Likes can be profitable, as a result of those cost savings, a large number of

12

companies implement marketing strategies to acquire them. Another study found that the average

13

cost of advertising on Facebook to encourage a user to become a Fan – “Like” the advertiser’s

14

Facebook page – was $1.07.104 This cost also varies across sectors and over time. In 2012, the

15

cost per acquired Fan (i.e., cost per click in Fan acquisition campaigns) averaged $0.55.105 These

16

costs are leveraged through targeting via the Social Graph as brands can gain seven times greater

17

CTR by targeting Fans with ads which keeps cost per click at a minimum.106

18

71.

Therefore, the direct incremental impact of the accused practice on Facebook is to

19

increase advertising revenue, in the form of cost savings to advertisers from the accrual of Likes

20

from the intercepted private messages.

21
22
23
24
25
26
27
28

104

Webtrends, White Paper, 2011. Reported in The Wall Street Journal, “How Much Does a
Facebook Fan Cost?” February 1, 2011.
105
Based on data in WebTrends®, “Ads for Fans”, 2012, p. 4.
106
Ibid, p. 2.
26

REPORT OF FERNANDO TORRES IN SUPPORT OF
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EXHIBIT A

FERNANDO TORRES, MSc
CHIEF ECONOMIST
Fernando Torres is an intellectual property economist with nearly 30
years of work experience in economics, financial analysis, and
business management in the U.S. and Mexico. He is a member and
Chief Economist at IPmetrics LLC, an IP consulting firm specializing in
the strategic analysis, valuation, and expert witness assessment of the
full spectrum of intangible assets.
Since 2004, Mr. Torres has applied his economics, finance and
business experience, as well as skills in quantitative techniques, to the
analysis and valuation of intangible assets, including valuation for transactional and litigation
purposes (bankruptcy and infringement cases). Prior to joining IPmetrics, Mr. Torres served as
Senior Economist at CONSOR® Intellectual Asset Management.
During recent years, Mr. Torres has undertaken projects involving the valuation and/or the
assessment of infringement damages regarding copyrights, trademarks, patents, trade secrets,
rights of publicity, and other intellectual assets in such industries as commercial agriculture,
auto parts, apparel and footwear, retail, pharmaceuticals, entertainment, telecommunications,
social media, as well as non-profit organizations, among others.
Mr. Torres regularly presents on topics related to intangible asset valuation in a variety of
venues, many of which qualify for CLE credit. During the past few years, Mr. Torres has been
an instructor for the course “Valuing Intangible Assets for Litigation,” which is part of the
requirements of the Certified Forensic Financial Analyst designation issued by the National
Association of Certified Valuation Analysts (NACVA).
Mr. Torres has been active in the area of the copyrights, privacy and rights of publicity
infringement issues, encompassing from the unlicensed use of celebrity images to class action
lawsuits involving the major social networking and web services companies.
Mr. Torres is also the editor and author of the online “Patent Value Guide” and his perspectives
on the value of patents and other intellectual property assets have been cited in the media,
including Managing Intellectual Property, The New York Times, Forbes.com, Business News
Network, Business Valuation Resources, and The Democrat & Chronicle.
Mr. Torres is a member of the National Association of Forensic Economics, and of the Western
Economics Association International, among others. His career has spanned from academia, to
branches of government, to private industry and consulting.
He first earned a B.A. in Economics from the Metropolitan University in Mexico City (1980), and
went on to earn a Graduate Diploma in Economics from the University of East Anglia (U. K.,
1981), and a Master of Science Degree specializing in Econometrics from the University of
London, England (1982).

[e-mail]

www.ipmetrics.com

Fernando Torres
Qualifications and Experience
Page 2

Prior to specializing in IP, his career centered on financial analysis and management in the
private sector, having been both a brand development consultant and an entrepreneur in
several business ventures, mainly in the software development and health care industries.
During the 1980s, Mr. Torres was Professor of Economics at the Metropolitan University in
Mexico City, teaching Economic Policy, Economic Growth, Microeconomics, and Quantitative
Methods. Mr. Torres was later a financial consultant (NASD Series 7, 63, 65) for half a dozen
years with AXA Advisors LLC.

PROFESSIONAL ASSOCIATIONS





National Association of Forensic Economics
Western Economics Association International
American Economic Association
International Trademark Association

PUBLICATIONS


“Why only some patents are valuable” in: IPmetrics Blog, (May 13, 2015).



“General Principle I – Lack of Intrinsic Value” in: PatentValueGuide.com, (February
11, 2013).



“General Principle II – Patent Use is Key to Value” in: PatentValueGuide.com,
(February 8, 2013).



“Conceptual Patent Value Framework” in: PatentValueGuide.com, (January 31,
2013).



“The Impact of Reorganization on Trademark Values,” in: IP Management and
Valuation Reporter, March 2012, BVR, Portland, OR.



“Fundamental Principles of Patent Value,” in: IP Management and Valuation
Reporter, January 2012, BVR, Portland, OR.



“Key Factors of Infringement Damages Apportionment in the Java & Android Case”
in: IPmetrics Blog, (December 8, 2011).



Book Chapter: “Valuation, Monetization, and Disposition in Bankruptcy” in IP
Operations and Implementation for the 21st Century Corporation, John Wiley and
Sons, Inc. (November, 2011).



“Have Patent Litigation Damages Awards Been Worth It?” in: IPmetrics Blog, (April
29, 2011).



“Celebrity Advertising and Endorsement” in: IPmetrics Blog, (March 2, 2011).



“The Patent to Trademark Value Transition: Nespresso” IPmetrics Blog, (February 3,
2011).

[e-mail]

www.ipmetrics.com

Fernando Torres
Qualifications and Experience
Page 3



“The Liquidation Value of IP” in: IPmetrics Blog, (January 26, 2011).



“An Econometric Model of Trademark Values” in: IPmetrics Blog, (January 25,
2011).



Chapter 15: “Copyrights” in Wiley Guide to Fair Value Under IFRS, John Wiley and
Sons, Inc. (May, 2010).



“The Road to Asia,” Feature Article (co-author) in: World Trademark Review, No. 23,
February/March 2010, pp. 19-22.



"Trademark Values in Corporate Restructuring" (July, 2007). Social Sciences
Research Network: http://ssrn.com/abstract=1014741



“Establishing Licensing Rates Through Options”
(September, 2006) Social
Sciences Research Network: http://ssrn.com/abstract=1014743
and in:
http://formulatorres.blogspot.com/2006_05_01_archive.html



Book Chapter: “Ch. 9: Recent developments in Patent Valuation” in: Practicing Law
Institute, Patent Law Institute 2007: the Impact of Recent Developments on Your
Practice, PLI Course Handbook (March 19, 2007).



“Establishing Licensing Rates through Options,” in: ipFrontline, September 12, 2006
(http://www.ipfrontline.com/depts/article.asp?id=12586&deptid=3).

COURSES AND PRESENTATIONS


“What is a Brand Worth?” MCLE webinar, The State Bar of California, Trademark
Interest Group, March 2015.



“Intellectual Property Valuation Techniques,” MCLE presentation for Pillsbury
Winthrop Shaw Pittman, San Diego, CA, August 2014.



“10 Common Mistakes in IP Valuation/Damages”, CLE presentation to Jeffer
Mangels Butler & Mitchell LLP, Los Angeles, CA, July 2014.



“Intellectual Property Valuation Techniques,” MCLE presentation, San Diego, CA,
April 2013



“Intellectual Property Valuation and Monetization,” a seminar for the Special
American Business Internship Training (SABIT) Intellectual Property Rights program,
U.S. Department of Commerce. March, 2013.



“Valuing IP in the Context of Bankruptcy,” webinar for the Certified Patent Valuation
Analyst curriculum, Business Development Academy. October, 2011.



“Recent Developments in Intellectual Property Economic Damages,” Presentation at
the Annual Conference of the National Association of Forensic Economics. June,
2011.



“Valuing the Intangible: Where to Start?” CLE presentation to Sheppard Mullin
Richter & Hampton, LLP. December, 2009.

[e-mail]

www.ipmetrics.com

Fernando Torres
Qualifications and Experience
Page 4



“Defending and Enforcing Your Technology.” Panelist at: Foley’s Emerging
Technologies Conference: Navigating a New World – San Diego, CA (Foley &
Lardner LLP); September 2009.



”Intellectual Property Valuation, Monetization and Disposition in Bankruptcy” – CLE
presentation at the Spring Trademark Program of the NY Intellectual Property Law
Association – New York, NY; June 2009.



“Damages Valuation and Expert Witnesses” (co-presenter) – CLE presentations to:
•
•
•

Gibson, Dunn & Crutcher LLP – Irvine, CA (June, 2008)
Arent Fox, LLP — Washington, DC (April, 2008)
Finnegan, Henderson, Farabow, Garrett & Dunner, L.L.P.
Washington, DC (April, 2008)

–



“Valuing Intangible Assets for Litigation” (Instructor) – National Association of
Certified Valuation Analysts (NACVA) – Fort Lauderdale, FL; December 2007



“Valuing Intangible Assets for Litigation” (Instructor) – National Association of
Certified Valuation Analysts (NACVA) – Philadelphia, PA; October 2007



“Trademark Values in Corporate Restructuring” – Western Economics Association
International 82nd Annual Conference – Seattle, WA; July, 2007



“Entrepreneurship and Innovation” (Session Chair) – Western Economics
Association International 82nd Annual Conference – Seattle, WA; July, 2007



“Alternative Focuses for ‘But For’ Scenario Specification in Commercial Litigation”
(Discussant) – National Association of Forensic Economics, Western Conference –
Seattle, WA; June, 2007



“Patent Values in the Evolving I.P. Market” – Practicing Law Institute – Hot Topic
Briefing Teleconference; May 2007 (CLE Presentation)



“Key Issues in Intellectual Property Due Diligence” – Due Diligence Symposium
2007 – ACG – Iselin, NJ; April 2007



“Life Sciences IP Due Diligence” – American Conference Institute – San Francisco,
CA; January 2007



“Developments in Patent Valuation” – Practicing Law Institute – San Francisco, CA;
January 2007 (CLE Presentation)



“Collins & Aikman Europe and Other Cross-Border Asset Sales: A Tale of Two
Venues” – American Bankruptcy Institute, Winter Leadership Meeting – Phoenix, AZ;
December 2006



“Valuing Intangible Assets for Litigation” (Instructor) – National Association of
Certified Valuation Analysts (NACVA) – San Diego, CA; December 2006.

[e-mail]

www.ipmetrics.com

Fernando Torres
Qualifications and Experience
Page 5

LITIGATION-RELATED EXPERIENCE
(Last Four Years)
Date
Range
February
2012

March
2012

Parties

Case No.

Court

Status

The Int’l. Aloe
Science Council
Inc. V. Fruit of
the Earth, Inc.

11-CV-2255 United States Settled
District Court

A. Fraley, et al v.
Facebook, Inc.

11-CV-1726 United States Settled
District Court

District of
Maryland

Nature

Hired by

Involvement

Trademark
Infringement.

Kane
Kessler,
P.C.

Expert
Rebuttal
Report on
Damages,
Depositions

Rights of
Publicity

The Arns
Law Firm

Expert
Declarations
in Support of
Motion for
Class
Certification,
Value of
Injunctive
Relief,
Deposition

Northern
District of
California

Class
Action

Superior
Closed
Court of the
State of
California
(Los Angeles)

Rights of
Publicity

Wilson
Elser
Moskowitz
Edelman &
Dicker LLP

Preliminary
Expert
Damages
Report,
Arbitration

Contract,
Database

Neymaster
Goode, PC

Expert
Damages
Rebuttal
Report,
Deposition,
Arbitration

Copyright
&
Trademark
Infringement

Ezra
Brutzkus
Gubner
LLP

Expert
Damages
Report,
Deposition

Intangible
Asset
Fair
Market
Value

Tripp Scott
PA

Declaration,
Expert
Damages
Report,
Deposition

Patent
Infringement

Ferraiuoli,
LLC

Expert
Damages
Report,
Deposition

Contract,
Software
IP value

Kalbian
Hagerty
LLP

Expert
Rebuttal
Reports,
Depositions,
Trial
testimony

August
2013

Jude Law v.
Paloform Inc.

SC120354

September
November
2013

Scidera, Inc. v.
Newsham
Choice
Genetics, LLC

American
AAA 16174-00582- Arbitration
Association
12

February
2014

Lambert Corp.
v.
LBJC, Inc.et al.

13-CV-0778 United States Settled
District Court

S. Mattocks v.
Black
Entertainment
Television LLC

13-CV61582

April 2014

July – Aug.
2014

Aug. 2014Aug. 2015

Tierra
Intelectual
Borinquen, Inc.
v.
Toshiba
Corporation.
S. Abu-Lughod
v. S. Calis,
Tocali, Inc.,
ASCII Media,
Inc., et al.

[e-mail]

Closed

Central
District of
California
United States Closed
District Court
Southern
District of
Florida
13-cv-47

United States Settled
District Court
Eastern
District of
Texas

13-cv-2792

United States Closed
District Court
Central
District of
California

www.ipmetrics.com

Fernando Torres
Qualifications and Experience
Page 6

Date
Range
Feb – Mar
2015

Jan. - May
2015

Parties

Case No.

Court

Status

Nature

Hired by

Involvement

S. Nerayoff vs.
L. Rokhsar

2031572012

Supreme
Closed
Court Of The
State Of New
York

Value of
Patent
Assets

Baker &
Hostetler
LLP

Expert
Declaration
on Patent
Value,
Trial
testimony

In Re Google,
Inc., Privacy
Policy
Litigation.

12-cv-1382

United States Closed
District Court

Breach of
Contract
Class
Action

Grant &
Eisenhofer
P.A.

Expert
Report on
Privacy
Damages,
Deposition

[e-mail]

Northern
District of
California

www.ipmetrics.com

EXHIBIT B

Exhibit B - List of Materials Relied On:
I relied on the following documents and materials in forming my opinions:
Academic Literature
1.

Vogel, Harold L. Entertainment Industry Economics. Cambridge
University Press, 2011..

2.

Smith, Gordon V., and Russell L. Parr. Valuation of intellectual property
and intangible assets. Vol. 13. Wiley, 2000.

3.

Reilly, Robert F., and Robert P. Schweihs. Valuing intangible assets.
McGraw Hill Professional, 1998.

4.

Business Valuation Resources, “Benchmarking Identifiable Intangibles
and Their Useful Lives in Business Combinations” 2012, p. 66
(www.bvresources.com).

5.

Duff & Phelps, 2015 Valuation Handbook: Guide to the Cost of Capital,
John Wiley & Sons, 2015

6.

Loumioti, Maria. "The use of intangible assets as loan collateral." Harvard
Business School Job Market Paper (2011).,
(http://ssrn.com/abstract=1748675)

7.

Ugander, Johan, Brian Karrer, Lars Backstrom, and Cameron Marlow.
"The anatomy of the facebook social graph." arXiv preprint
arXiv:1111.4503 (2011). (http://arxiv.org/abs/1111.4503v1)

8.

Perdue, David J. "Social media marketing: Gaining a competitive
advantage by reaching the masses." Senior Honors Papers (2010): 127.

9.

Buhalis, Dimitrios, and Emmanouil Mamalakis. "Social media return on
investment and performance evaluation in the hotel industry context." In
Information and Communication Technologies in Tourism 2015, pp. 241253. Springer International Publishing, 2015.

10.

Goldfarb, Avi, and Catherine Tucker. "Shifts in privacy concerns."
Available at SSRN 1976321 (2011). (http://ssrn.com/abstract=1976321)

11.

Tucker, Catherine. "Social advertising." Available at SSRN 1975897
(2012). (http://ssrn.com/abstract=1975897)

12.

Tucker, Social Networks, Personalized Advertising, and Perceptions of
Privacy Control, Time Warner Research Program on Digital
Communications, Summer 2011
(http://209.59.135.49/pdf/TWC_Tucker_v3a.pdf).

Articles and Other Online Sources
1.

Business Insider, Business Intelligence Report on Social Engagement
(http://www.businessinsider.com/social-media-engagement-statistics2013-12).

2.

Business Insider Depiction of Social Graph
(http://static3.businessinsider.com/image/4f5112e169bedd1526000061
/facebook-open-graph.jpg).

3.

MarketingLand (http://marketingland.com/facebooks-latest-tweaks-favorfriends-could-hurt-page-reach-125931).

4.

W. Oremus, “That Facebook Copyright Notice Is Still a Hoax” November
26, 2012, Slate
(http://www.slate.com/blogs/future_tense/2012/11/26/facebook_copyright
_notice_berner_convention_status_update_still_a_hoax.html)

5.

Trade publication, Advertising Age, Nov. 22, 2011,
(adage.com/print/231128).

6.

Peter Elbaor, “The Interconnection of Facebook Fan Pages” October 28,
2011, ComScore Insights Blog,
(http://www.comscore.com/Insights/Blog/The-Interconnection-ofFacebook-Fan-Pages).

7.

Webtrends, white paper, 2011. Reported in The Wall Street Journal,
“How Much Does a Facebook Fan cost?” February 1, 2011. Based on data
in WebTrends®, “Ads for Fans”, 2012, p. 4.

8.

Facebook CEO Mark Zuckerberg’s public post on Facebook.com of
August 27, 2015, at:
(https://www.facebook.com/zuck/posts/10102329188394581)

9.

Wolfram|Alpha Knowledgebase, using data from the World Bank
(http://www.wolframalpha.com/ accessed 10/26/15).

10.

US Census projections and Statistics Canada estimates [In:
http://www.census.gov/population/projections/data/national/2014/summar
ytables.html, and http://www.statcan.gc.ca/pub/91-002-x/2015002/t002eng.pdf]

11.

“IAB Social Media Buyers Guide” by Facebook’s Adam Isserlis,
Manager, Corporate Communications, Ads/Monetization; Colleen Coulter,
Product Marketing Communications Manager available on the Interactive
Advertising Bureau website (http://www.iab.net/socialmediabuyersguide).

12.

Facebook for Developers website: https://developers.facebook.com/.

13.

“Instant Articles” initiative and new deals with publishers like the
Washington Post (http://media.fb.com/2015/05/12/instantarticles/).

14.

Expanding the power of Facebook search
(http://newsroom.fb.com/news/2015/10 /search-fyi-find-what-the-worldis-saying-with-facebook-search/).

15.

Video, with video hosting and action tracking
(http://newsroom.fb.com/news/2015 /06/news-feed-fyi-taking-intoaccount-more-actions-on-videos/), app acquisitions like Instagram and
WhatsApp, and with plugins to track activities outside of Facebook

16.

Google Products and Advertising Platforms
(www.thinkwithgoogle.com/products/)

17.

“Digital Advertising Report Q3 2015,” Adobe Digital Index
(www.cmo.com/adobe-digital-index.html), p.18, 24.

18.

Open Graph protocol, this is used on Facebook to allow any web page to
have the same functionality as any other object on Facebook. See:
http://ogp.me/.

19.

https://developers.facebook.com/docs/graph-api

20.

Facebook SDK Documentation
(https://developers.facebook.com/docs/javascript/quickstart/v2.5#plugins).

21.

J. Kincaid in: TechCrunch (http://techcrunch.com/2009/02/09/facebookactivates-like-button-friendfeed-tires-of-sincere-flattery/).

22.

Facebook Help Center (Each Facebook account has a unique username.
On a user’s timeline page, their username will appear at the top of the
browser and look something like www.facebook.com/[username]).
https://www.facebook.com/help/228578620490361.

23.

Facebook, Social Plugins FAQs, at:
https://developers.facebook.com/docs/plugins/faqs/#ref.

24.

Facebook for Business post on November 14, 2014
(https://www.facebook.com/business/news/update-to-facebook-newsfeed).

25.

Facebook Media, “An Update to News Feed: What it Means for
Businesses” (https://www.facebook.com/business/news/update-tofacebook-news-feed)

26.

“News Feed FYI: Balancing Content from Friends and Pages”
(http://media.fb.com/2015/04/21/news-feed-fyi-balancing-content-fromfriends-and-pages/).

27.

Facebook’s disclosure in connection with the redesign of the Messenger
platform, at: https://www.facebook.com/notes/facebook-engineering/theunderlying-technology-of-messages/454991608919

28.

Facebook, between August 2012 and May 2013 user engagement, as
illustrated in the number of likes generated per day, increased from 2.7
Billion to 4.5 billion on average

(https://www.facebook.com/photo.php?fbid=10151908376831729&set=a.
10151908376636729.1073741825.20531316728&type=1&theater).
29.

Nielsen “Quickly and Accurately Measure the Effectiveness of Your
Online Ad Campaigns” available as:
www.nielsen.com/content/dam/nielsen/en_us/documents/pdf/Fact%20She
ets/Nielsen%20BrandLift.pdf.

30.

National Public Radio, Planet Money “For $75, This Guy Will Sell You
1,000 Facebook 'Likes'” originally broadcast on May 16, 2012
(http://www.npr.org/sections/money/2012/05/16/152736671/this-guy-willsell-you-sell-you-1-000-facebook-likes).

31.

https://www.facebook.com/notes/facebook-engineering/tao-the-power-ofthe-graph/10151525983993920

32.

“Uniform Resource Locators (URL): A Syntax for the Expression of
Access Information of Objects on the Network” by Tim Berners-Lee
(March 1994) in: http://www.w3.org/Addressing/URL/url-spec.txt.

33.

US Census Bureau, Geographical Mobility: 2005 to 2010 (December
2012), Table 2, Page 5 (http://www.census.gov/prod/2012pubs/p20567.pdf).

34.

https://www.facebook.com/help/cookies/

Document produced by Defendant in Campbell et al. v. Facebook, Inc.
1.
2.
3.
4.
5.
6.
7.

FB000012475
FB000015766
FB000026790
FB000026793
FB000011745
FB000011715
FB000008271

Other Information
1.

Plaintiffs’ Consolidated Amended Complaint filed on April 25, 2014

2.

Facebook, Inc. 2014 10-K

3.

Facebook, Inc.’s 2015 Q3 Earnings Report (November 4, 2015) At:
http://investor.fb.com/results.cfm

4.

Facebook, Inc.’s 2015 Q2 Earnings Report, July 29, 2015

5.

Facebook, Inc. 2012 10-K

6.

Second Quarter, 2015 Earnings Call held on July 29, 2015. Available at:
http://investor.fb.com/results.cfm.

7.

Facebook securities registration statement (SEC Form S-1/A May 16,
2012 p 2).

8.

Facebook F8 Developer Conference, April 21, 2010.

9.

Facebook, Inc. 2010 10-K Disclosures

EXHIBIT 34
FILED UNDER SEAL

EXHIBIT 35
FILED UNDER SEAL