Campbell et al v. Facebook Inc.

Filing 157

EXHIBITS re 149 Opposition/Response to Motion,,,,, filed byFacebook Inc.. *** ATTACHMENTS 1, 13 LOCKED AT FILER'S REQUEST. SEE DOCUMENT 162 . *** (Attachments: # 1 Appendix of Evidence (Part 1 of 13) (Redacted), # 2 Appendix of Evidence (Part 2 of 13) (Redacted), # 3 Appendix of Evidence (Part 3 of 13) (Redacted), # 4 Appendix of Evidence (Part 4 of 13) (Redacted), # 5 Appendix of Evidence (Part 5 of 13) (Redacted), # 6 Appendix of Evidence (Part 6 of 13) (Redacted), # 7 Appendix of Evidence (Part 7 of 13) (Redacted), # 8 Appendix of Evidence (Part 8 of 13) (Redacted), # 9 Appendix of Evidence (Part 9 of 13) (Redacted), # 10 Appendix of Evidence (Part 10 of 13) (Redacted), # 11 Appendix of Evidence (Part 11 of 13) (Redacted), # 12 Appendix of Evidence (Part 12 of 13) (Redacted), # 13 Appendix of Evidence (Part 13 of 13) (Redacted))(Related document(s) 149 ) (Chorba, Christopher) (Filed on 1/16/2016) Modified on 1/22/2016 (ewn, COURT STAFF). Modified on 1/22/2016 (vlkS, COURT STAFF).

App. 2021

counter from 765 to 766 would have affected me or influenced other’s subsequent
behavior.
40.

Further, any analysis of whether there was any real effect of an increment in the social
plugin counter is complicated by the set of technical circumstances that need to be met
for a social plugin counter to have incremented. For example, I understand that if
.35

Figure 4: Sharing a Story about Halloween with a Friend on Facebook

Figure 5: The story that I shared with my Friend (as of 2015)36
41.

For the Halloween story example depicted in Figure 4, it is difficult to imagine how I
would have been adversely affected if the information were used, as alleged by the
Plaintiffs, in a recommendation algorithm that tried to highlight interesting content in a

35
36

Declaration of Alex Himel ¶¶ 28.
Fisher, Max, “Why Australia Hates Halloween,” Vox, October 31, 2014,
http://www.vox.com/2014/10/31/7137369/why-australia-hates-halloween, viewed January 6, 2016.

17

App. 2022

social plugin displaying recommendations on the Vox website should the primary system
for providing such recommendations have failed.
42.

It is also unclear how I would be affected if Vox had accessed the Insights tool or
associated APIs, as alleged by the Plaintiffs, had learned that their audience was slightly
more female, closer in age to forty, and more English-speaking than before, especially as
I have visited their website on other occasions, meaning that Vox would have presumably
already accessed this information and there would be no new incremental information. Of
course, as this happened in 2014, rather than prior to October 2012, this could not have
happened in any case.

43.

In general, this second example illustrates that even if one supposes a relevant social
plugin was present on the website for which the URL attachment was created, trying to
identify whether or not the potential for an increment on the social plugin counter had
any meaningful effect on anyone is difficult (and sometimes impossible). Furthermore,
the aggregate and anonymous nature of the data collected limits effects of the other
disputed practices in the time periods when the occurred.
B.

44.

Some potential class members benefited from the challenged practices

Plaintiffs’ Motion for Class Certification suggests that Facebook “monetizes the content
of these private messages for its sole benefit.”37 However, my analysis suggests that many
people who use Facebook benefit directly from the usage of URL share counts to allow
them and others to identify relevant and useful websites. In this section, I lay out two
potential ways that people who use Facebook may benefit.
1.

45.

Some proposed class members benefited directly from incremental
publicity

First, Plaintiffs who shared URLs in which they had a direct financial or vested interest in
publicizing may have benefited directly from this practice. For example, Mr. Campbell
stated that
.”38 As a consequence,

37
38

Plaintiffs’ Motion for Class Certification at 1.
Campbell Depo. Tr. at 45:1.

18

App. 2023

App. 2024

Figure 6: URL sender actively seeks Social Media Activity surrounding the URL
47.

Given that the website owner was actively soliciting social media activity in order to
boost the perceived popularity of his website, he would have directly (and
unambiguously) benefited from any incrementing of the internal social plugin counter for
the website as a result of sending me this message.41 In a case such as Figure 6, where the
owner was actively seeking publicity, anything that would boost the likelihood of his
website being recommended would benefit him though at this distance the website does
not appear to have a social plugin displaying either the Recommendations or Activity
Feed. Furthermore, since he is presumably already constantly visiting his own website,
his own demographics being shared with him make no difference to him.

48.

This is not an isolated example. For example,
.42 Although

41

42

See Events Insider, http://bostoneventsinsider.com/subscribe html/, viewed December 17, 2015 for details. In
this case it seems apparent that “Johnny” clearly benefited from the disputed practices by Facebook.
See Plaintiff Matthew Campbell’s Corrected Objections and Responses to Defendant Facebook, Inc.’s First Set
of Interrogatories. This series of messages are summarized by rows messages 409, 411, and 412.

20

App. 2025

App. 2026

Figure 7: Message where I indirectly benefited
51.

Since I shared this message depicted in Figure 7 too recently for it to benefit me by
potentially incrementing the social plugin counter (because Facebook ceased this practice
in 2012), the main avenue of benefit would be if

.
However, as of 2015, it does not seem that such a social plugin exists.44 If my church
accessed the Insights tool or associated APIs, there is a chance they would believe that
their audience was (slightly) more female than before and perhaps closer to forty than
before. Since I already provide far more detailed information to them as a member, and
my gender and age are apparent to them every Sunday I attend, I am not sure how this
would affect anything. Of course, because this is after October 2012, there is no
possibility that this occurred.
52.

In a similar spirit, there are examples among the Named Plaintiffs where there are
potential indirect benefits. Plaintiff Hurley
which is
shown in Figure 8.

44

See Home: Old South Church, http://oldsouth.org, viewed January 11, 2016.

22

App. 2027

Figure 8: Message to Plaintiff Hurley, as produced by Plaintiffs45
53.

Although I cannot be sure without viewing the content of the message that was redacted,
.46
.
Plaintiff Hurley’s

.
54.

Similarly, Mr. Campbell
.47 Again, it seems likely that Mr. Campbell

e.
C.
55.

It is difficult to determine the effect of the at-issue practices on some
potential class members

One issue for assessing whether proposed class members were negatively affected by the
disputed practices in this case is that the Named Plaintiffs in their depositions revealed
that they have divergent ideas of what negative effects they could potentially have
suffered which also are not necessarily based on fact or the current class certification
motion.

45
46
47

HURLEY000001.
See Hartner Depo. Tr. at 157:18-160:7.
See Plaintiff Matthew Campbell’s Corrected Objections and Responses to Defendant Facebook, Inc.’s First Set
of Interrogatories, Exhibit 1.

23

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App. 2029

58.

The above testimony demonstrates the difficultly in determining if potential class
members have been harmed by the challenged behavior. In order to assess harm from an
economics perspective, one must have a clear definition of what is harmful, which the
Plaintiffs have failed to consistently provide.
1.

59.

A “Like” button does not necessarily imply endorsement

Let us start with the most concrete statement of harm which was Plaintiff Campbell
stating that “
.”57

60.

Underlying this argument appears to be the assumption that a “Like” is unambiguously
an endorsement. However, it is not clear that Facebook or more general web users view it
as such. Table 1 reports results from a marketing research survey conducted by
ExactTarget where they asked people who use Facebook why they “Liked” a company’s
webpage.58 What is immediately striking is that there are many different reasons why
people click “Like.” Table 1 shows that even in 2010, only 39 percent of users used the
“Like” button to “show my support of the company to others.” Instead, there are a myriad
of ways that the “Like” button was being used that do not necessarily imply
endorsement.59 This multi-purpose use of the “Like” button means that users already
anticipate that a count of Likes does not necessarily imply multiple endorsements, but
could derive either from users wanting discounts or offers from a particular website or
because they wanted to stay informed (for whatever purpose).

57
58

59

Campbell Depo. Tr. at 190:7-10.
According to the webpage, the survey was fielded from April 9, 2010 through April 13, 2010. The survey was
fielded through a MarketTools TrueSample online panel and completed by 1,506 U.S. respondents, aged 15 and
older, and stratified by age so that each age bracket contained no less than 200 responses. Responses are
weighted by age and gender according to U.S. Census Bureau population estimates and Pew Internet Project’s
online activity data to reflect the online U.S. consumer population.
A recent paper by researchers from Harvard found that consumers respond enthusiastically to invitations to Like
brands – popular or unpopular, new or established – and that such indiscriminate “Liking” suggests that
expressing a “Like” may not reflect deep preferences. John, Leslie et al., “What are Facebook ‘Likes’ Really
Worth?,” HBS Working Paper, 2015, http://rady.ucsd.edu/docs/events/lesliejohn.pdf.

25

App. 2030

Table 1: Why do people click “Like” for a company, brand, or association?
Motivation
To receive discounts and promotions
To show my support for the company to others
To get a ‘freebie (e.g., free samples, coupon)
To stay informed about the activities of the company
To get updates on future products
To get updates on upcoming sales
For fun or entertainment
To get access to exclusive content
Someone recommended it to me
To learn more about the company
For education about company topics
To Interact (e.g., share ideas, provide feedback)

Percentage
40%
39%
36%
34%
33%
30%
29%
25%
22%
21%
13%
13%

Source: “The Thin Line between Liking a Brand and Liking Its Social Marketing,”
eMarketer, September 8, 2010, http://www.emarketer.com/Article.aspx?R=1007912,
viewed January 8, 2016.
61.

Mr. Torres testified about one particularly clear example where a “Like” is not an
endorsement: “Facebook has hinted at introducing other alternatives for people to express
their response or reaction to posts and things like that” because, for example, “it’s always
been a curious thing that if somebody posts a death or reports a death in the family, that
the summary way to show your, your awareness of the message, or anything else, is to
click on ‘[l]ike.’”

62.

Such variance makes it difficult to assess whether potential class members have been
harmed and whether that harm is common across class members because the context in
which URLs are shared varies across messages and that context cannot be known without
individual inquiry.
2.

63.

Due to the use of aggregate counts it is very unlikely any single increment
of the social plugin counter had a negative effect for that individual

As well as a Like not necessarily implying endorsement, it is unlikely that a small
perturbation in the number of “Likes” displayed on a social plugin counter due to the
sharing of a URL by one individual will affect outcomes substantially and any potential
effect will vary substantially by website and time. Indeed, Mr. Torres explained this in
his deposition by pointing that a website with “[l]ike counts of, in the order of one or two,

26

App. 2031

then it’s a 100 percent increase” in the count. However, if the social plugin incremented
was for “Coca Cola, and they already have 500,000 ‘Likes’ on their third-party website,
that is a miniscule less than a 1 percent, so, they won’t be as influenced or as impressed
by the increase.”60 In other words, even if there is an effect, the effect would not be
common across potential class members and would depend on the nature of the URL
shared and the date.61
64.

Though this analysis focuses on the potential for negative effects of sharing, it also
applies for the potential positive effects of sharing. In many cases, due to the small likely
effects of any one potential increment of the social plugin counter, the potential for
positive indirect benefits of the type discussed in Section VI.B is small. It seems more
likely that there would be a positive effect in the cases described in Section VI.A, simply
because an individual promoting their own website via messages is more likely to create
the volume of URL attachments that could lead to a more sizable increase in the social
plugin counter should it be in a context where that was a possibility.
3.

65.

It is difficult to determine potential negative effects of any sharing of a
URL without intrusive inquiry

The facts that Likes are not necessarily interpreted as endorsements and that the potential
marginal effects of any one Like on a counter is small, limit any potential negative effects
from the alleged practices. However, even without these constraints, there are only very
unusual and individualized circumstances where I can envisage harm. Indeed, the only
circumstance I can identify when there could have been a potential negative effect on

60

61

Torres Depo. Tr. at 174:3-175:4 (“Q. Why does it make it appear that the integration is more effective than it is?
A. Because the like count is increasing, despite the fact that the person is not clicking on the like button on the
third party website. Q. And does that opinion depend on how much the like counter is increasing, based on
messages? A. Not necessarily. Q. Why not? A. Because it depends, it would depend on exactly what the
proportion of the enhancement is. During some, at some point, according to some of the experiments reported
on The Wall Street Journal, the like count was increasing twice, or, or, in a two-to-one ratio, to including the
URLs in the messages. So, if that happens to a website, a third party website that has like counts organic like
counts of, in the order of one or two, then it’s a 100 percent increase. If it happens to Coca Cola, and they
already have 500,000 likes on their third party website, that is a miniscule less than a 1 percent, so, they won’t
be as influenced or as impressed by the increase.”).
This is further complicated by the fact that rather taking notice of absolute numbers of social plugin counts,
consumers are more influenced by the location of the link on the homepage. This is something demonstrated
with my research that shows the importance of website location relative to the influence of popularity
information. Tucker, Catherine, and Juanjuan Zhang, “How Does Popularity Information Affect Choices? A
Field Experiment,” Management Science, Vol. 57, No. 5, 2011, pp. 828-842.

27

App. 2032

people who use Facebook would be if the proposed class member shares a URL with a
friend that they wanted to alert their friend about, but they would prefer for other people
to not visit the URL. It is difficult to imagine how to determine this rather nuanced and
complex set of circumstances without a great deal of individual inquiry.
66.

Indeed, I found it problematic to identify a straightforward example of a URL being
shared in a message that the sender would prefer not to be publicized. The closest
example I can find is as follows. My husband chairs a Fourth Amendment organization
called “Restore The Fourth,” whose previous website was at www.restorethefourth.net,
and whose current website is at www.restorethe4th.com. He shared a message over
Facebook

with

a

colleague

in

October

2015,

regarding

the

former

URL

“restorethefourth.net”. In the message, he noted that [an unknown] someone was
updating that URL. It could be argued that my husband would prefer traffic where
possible to not be diverted to restorethefourth.net as a result of his message, as he was
questioning whether having two parallel websites was potentially confusing. However,
even in this case – supposing it was affected by the alleged practices, which it was not
since the message was sent in October 2015 – it is not straightforward.

Figure 9: Example of a message where a social plugin count of the URL in the message did
not necessarily benefit the sharer
67.

Restorethefourth.net

has

no

apparent

social

plugin.

From

its

appearance,

restorethefourth.net is not advertising-supported or linked to Facebook in any way. Of
course, this would have to be verified, and one issue my husband faced in managing this
issue is that he is not sure who has control of this website and has been unable to find this
out from the domain registrar who hosts the website.

28

App. 2033

68.

If there had been an operational social plugin displaying a counter or Recommendations
Feed which led to the website content being somehow boosted, it is not clear that my
husband is harmed. He is not averse to the content of the website, but wants to be able to
coordinate messaging for the Restore the Fourth movement better across websites.
Generally, he would prefer that more people actively contact their Congress member to
express support for the Fourth Amendment, which is what the reactivated
www.restorethefourth.net was trying to do. Indeed, he would prefer the URL to be
recommended over any other URLs (such as celebrity gossip websites), with the sole
exception of the more current and comprehensive URL for restorethe4th.com. Finally, the
degree of harm, if any, is likely to change over time, as his organization may be able to
contact and work with the individual who revived the old URL.

69.

It seems unlikely that the website in question accesses the Insights tools or related APIs
from Facebook, but if they do, again it seems immaterial whether or not my husband’s
demographic data is included in their data, since he is representative of many of their
supporters and had visited the website before emailing the URL to his colleague. And
again, because the message was sent in October 2015, there was no potential for any
social plugin counter or Insights to be affected.

70.

Another example of this ambiguity over the potential for negative effects is the instance
of Mr. Campbell sharing the URL in Figures 10-11, which is a
. Given that

.
71.

29

App. 2034

Figure 10: Message sent by Plaintiff Campbell, as produced by Plaintiffs62

62

CAMPBELL000075-77.

30

App. 2035

App. 2036

App. 2037

with increments to the social plugin counter. This methodology reflects a measure of the
costs a URL owner may have faced of obtaining the “Likes” through other means or the
benefits they may have obtained. This analysis is removed from any actual harm, and also
highlights the huge degree of variation and lack of commonality in the proposed
methodology.68
74.

Mr. Torres also opines in his Report that “Class membership [is] identifiable and
ascertainable based upon Facebook’s records.”69 However, Mr. Torres made clear during
his deposition that he was not offering an opinion on ascertainability70 and when asked
about paragraph 11.a. of his Report, stated that the “technical issue as to what records to
look at to identify the membership in the class, that’s not, that’s outside of my scope.”71
Therefore, my rebuttal of his report does not consider ascertainability; this is instead
addressed in the technical Report of Dr. Benjamin Goldberg.

75.

In my rebuttal to the Torres Report, I begin by observing that Mr. Torres has not
calculated “damages” to putative class members, but rather alleged “benefits” to
Facebook. I then consider each of the proposed methodologies in turn and whether these
two methodologies can be reconciled with each other. Last, I consider whether this
analysis informs underlying factors that relate to the appropriateness of statutory damages
from an economics perspective.
A.

76.

Mr. Torres estimated “benefits” to Facebook, not “damages” suffered by
putative class members

Although his report claims to describe the “Measure of Damages,”72 both of Mr. Torres’s
methods for estimating damages purport to be related to the benefits received by

68

69
70
71

72

The Plaintiffs’ Motion for Class Certification explains Mr. Torres’s second proposal as follows: “In addition,
Facebook generated value from its inflation of third-party Like counters. The economic benefit derived by
Facebook attributable to this conduct lies between two bounds: a higher bound represented by the cost that
client websites saved by not having to acquire additional Likes; and a lower bound determined by the market
value of artificially acquired Likes.” Plaintiffs’ Motion for Class Certification at 22.
Torres Report, ¶ 11.a.
Torres Depo. Tr. at 34:2-3 (“Q. [Are you offering an opinion on] [a]scertainability? A. No.”).
Torres Depo. Tr. at 93:7-14 (“Q. And are you offering an opinion in this case that class membership is
identifiable and ascertainable based upon Facebook’s records? A. To the extent that’s a technical issue as to
what records to look at to identify the membership in the class, that’s not, that’s outside of my scope.”).
Torres Report Section IV heading.

33

App. 2038

Facebook. This is confirmed in his deposition when Mr. Torres repeatedly noted that he
estimated the benefits allegedly received by Facebook, not damages suffered by putative
class members. For example, Mr. Torres stated: “So my report and methodology they
developed was asked to analyze the benefits to Facebook. So that’s, so, it doesn’t
calculate the detriment to the class members, or the potential class members, because it
wasn’t meant to.”73 Mr. Torres reiterated this several times in his deposition.74
77.

Therefore, Mr. Torres has not presented any method for estimating the actual damages or
loss, if any, suffered by individual putative class members. Furthermore, there is no
attempt to consider or evaluate any benefits enjoyed by putative class members and
integrate these into an evaluation of net damages.
B.

It is not clear what the proposed methodology relating to the Social Graph is
or why the alleged practices are being related to advertising
1.

78.

Summary of Mr. Torres’s method for estimating the alleged benefit to
Facebook of enhancing the “Social Graph”

Mr. Torres does not present a finalized methodology for estimating the benefit he alleges
Facebook received from enhancing the “Social Graph.” Instead he has “[laid] out the
methodology and the beginnings of the calculations that can be done with publiclyavailable information.”75 He states that he has not “finalized the calculations because I
haven’t received the precise data from Facebook.”76

73

74

75

76

Torres Depo. Tr. at 48:11-21 (“Q. Why doesn’t it examine, your methodology examine, instead of examining
benefit to Facebook, why doesn’t it examine detriment to the putative class? A. So, my report and methodology
that I developed was asked to analyze the benefits to Facebook, so that’s, so, it doesn’t calculate the detriment
to the class members, or the potential class members, because it wasn’t meant to.”).
See, e.g., Torres Depo. Tr. at 48:23-49:1 (“Q. So, you have not developed a methodology to calculate damages
to putative class members[?] A. That, that was not my task, no.”); 108:11-17 (“Q. . . . [H]ave you attempted to
calculate detriment to the putative class? A. As I said, that, that’s not part of my scope. My scope is to analyze
the benefits to Facebook.”); 279:7-11 (. . . [T]he methodology is attributing, is not measuring the effect, the
detriment, for example, to the class member, so it’s allocating to class members as a whole the benefits to
Facebook as a whole.”).
Torres Depo. Tr. at 107:2-9 (“Q. And do you lay out these calculations anywhere in your report? A. Well, in the
body of the report, in section 4, I lay out the methodology and the beginnings of the calculations that can be
done with publicly-available information. I haven’t finalized the calculations, because I haven’t received the
precise data from Facebook.”).
Torres Depo. Tr. at 107:2-9. I understand that Plaintiffs have not even requested most of this information from
Facebook. Declaration of Christopher Chorba ¶ 8.

34

App. 2039

79.

The methodology that Mr. Torres does present is premised on the assumption that
Facebook used information gathered from messages to expand and enhance the “Social
Graph,” and thereby allow Facebook to enhance the “value of its own social media
advertising platform.”77

80.

The most concrete statement of his methodology appears in paragraph 51: “Therefore, the
economic value of the benefits Facebook derives from the unlawfully gathered user URL
links is proportional to the impact of this additional information on the total information
on the Social Graph. In principle, the benefit to Facebook in this respect would be
measured by attributing the corresponding portion of the incremental value of the Social
Graph to the accretion of the unlawfully gathered links.” Mr. Torres then goes on to say
that the value of the Social Graph is the “product of the number of links (L) in the
Graph.”78

81.

In other words, Mr. Torres intends to calculate the benefit to Facebook by multiplying his
estimate of the value of the Social Graph, multiplied by the percentage of links in the
Social Graph obtained from Facebook messages as a percentage of all links in the Social
Graph.
2.

82.

Mr. Torres’s method is based on a false assumption

Mr. Torres’s methodology is based on the assumption that Facebook uses information it
obtained from Facebook messages to refine its targeting and increase advertising
revenues.79 However, Facebook did not incorporate any information from Facebook

77
78

79

Torres Report ¶ 36.
Torres Report ¶ 52. Mr. Torres also gives an equation for damages, D, which equal (Lt+1 – Lt)wt, where Lt+1 is
the next period’s number of links and Lt is today’s number of links. It is unclear what is meant by “next period”
and “today” in this equation. These labels may actually be intended to contrast the actual world with the “but
for” world where there was no counting of aggregate numbers of any URLs in messages. However, that is not
specified or clear. wt is the value of each link. It is also unclear what is meant by links or how they relate to the
storage of aggregate URL counts.
Torres Depo. Tr. at 45:3-13 (“Q. And what, based on your understanding of the allegations in the complaint,
and your assumption that those allegations are true, what was the benefit to Facebook, as you understand it? A.
Well, the accumulation of the information gleaned from the messages, basically, the edges between members
and the marketers and entities identified by the URLs, is accessible through, as part of the social graph, it’s
accessible to Facebook in developing the targeted advertising services that, that generate this revenue.”).

35

App. 2040

App. 2041

share similar interests and be interested in similar products and services.86 However, this
research highlights that it is the social connections that make Social Graph data
potentially valuable.
85.

However, none of the disputed practices embedded any social relationships in connection
with their use of URLs in messages. It was not the case that Facebook used or could have
used URL aggregate counts to identify the nature or intensity of social relationships.
Indeed, the inherent value of such aggregate URL share data is hugely diminished by the
fact that aggregate counts of website visitation are broadly and freely (or at least
inexpensively) available from many websites and providers such as Alexa, Compete,
Hitwise and comScore.87 Therefore, even supposing there was some link, which there is
not, between the alleged practices and advertising, it is unclear why a Social Graph is
relevant for Mr. Torres’s analysis.

86.

Indeed, more generally, the Social Graph does not drive all advertising revenue at
Facebook and the extent to which drives advertising revenue has changed over time. as
noted by AdAge in 2013, “Facebook has since introduced its ad exchange, FBX, and has
shifted its focus from social ads to more traditional web-advertising models, such as retargeting.”88,89 that do not rely on social relations. Furthermore, since 2013, Facebook has
also offered advertisers the potential to use custom audiences which offers access to an

86

87

88

89

This is a point emphasized by Dr. Golbeck in her TEDxMidAtlantic talk at minute 4:40 – the technical term
which she refers to in her talk for this idea is “homophily.” Golbeck, Jennifer, “The Curly Fry Conundrum:
Why Social Media ‘Likes’ Say More than You Might Think,” TEDxMidAtlantic 2013,
https://www.ted.com/talks/jennifer_golbeck_the_curly_fry_conundrum_why_social_media_likes_say_more_th
an_you_might_think, viewed December 11, 2015. Dr. Golbeck expanded on this in her deposition: “A. Yeah, so
homophily, H-O-M-O-P-H-I-L-Y, is a concept from sociology actually that basically birds of a feather flock
together that we tend to be friends with people who share our traits more than people randomly pulled from the
general population would share our traits.” Golbeck Depo. Tr. at 101:7-13.
See my recent paper, “Can Big Data Protect a Firm from Competition?,” jointly with Anja Lambrecht, for a
richer discussion of this point. Tucker, Catherine, and Anja Lambrecht, “Can Big Data Protect a Firm from
Competition?” December 18, 2015, SSRN (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2705530).
Delo, Cotton, and Michael McCarthy, “GM Returns to Facebook Advertising after Public Split a Year Ago,”
AdvertisingAge, April 9, 2013, http://adage.com/article/digital/gm-returns-facebook-advertising-publicsplit/240785/, viewed January 3, 2016. This is the same trade publication noted by Mr. Torres’s Report in
Footnote 101.
See, e.g., Delo, Cotton, “Facebook Launches New Retargeting Alternative to FBX: Targeting to Use Tracking
Software That Marketers Can Attach to Websites and Mobile Apps,” AdvertisingAge, October 15, 2013,
http://adage.com/article/digital/facebook-launches-retargeting-alternative-fbx/244746/, viewed January 3, 2016.
This describes an advertising platform that is based on behavior outside of the Social Graph.

37

App. 2042

audience based on the audience the advertiser already has, such as email addresses or
phone numbers.90 Such analysis is further complicated by the fact that for any one click
there may be several drivers which interlink in complicated ways, making identifying
what drives any one piece of advertising revenue problematic. This is unsurprising given
literature in economics which highlights the difficulty of measuring the economic drivers
of advertising effectiveness.91
3.
87.

The parts of the proposed methodology where Mr. Torres does give details
have several flaws

As discussed, Mr. Torres’s proposed methodology is unrelated to how Facebook
benefited from the challenged behavior, as it did not use the aggregate stores of
anonymous social plugin count data to target advertising, which is the fundamental
assumption of his methodology. However, even supposing that the data collected was
related to advertising (which it was not), issues remain with Mr. Torres’s three
calculations.

88.

The first calculation in Mr. Torres’s description of this methodology is a table of
estimated messages (Table 2 in his Report, at page 19). However, the total number of
messages seem irrelevant to the key aspect of the data which is needed, which is how
many of these messages had URLs that created attachments. 92 Crucially, even a count of
URLs that generated a URL attachment does not reflect whether they were used in any
disputed practice—that is, whether the data was used as part of a social plugin counter
between 2011 and 2012, or used in the background in the provision of aggregate

90

91

92

“More Matching Capabilities with Custom Audiences,” Facebook Marketing Partners, November 30, 2015,
https://facebookmarketingpartners.com/partner-news/more-matching-capabilities-with-custom-audiences/,
viewed January 3, 2016. Facebook offers potential advertisers a number of ways of targeting customers beyond
relationships. Specifically, on its business website, Facebook offers that advertisers can target users not only
through location variables such as country, state, zip code, or local area but also through demographics, user
selected interests, and shopping or use behavior. See “Facebook Advertising Targeting Options,” Facebook for
Business, https://www facebook.com/business/products/ads/ad-targeting/, viewed January 6, 2016.
Lewis, Randall A., and Justin M. Rao, “The Unfavorable Economics of Measuring the Returns to Advertising,”
The Quarterly Journal of Economics, first published online July 6, 2015 doi:10.1093/qje/qjv023.
Torres Report ¶ 45. In his deposition he appeared to restate this to say, “[t]he data that I would need is mainly
the number of those messages that were intercepted that contained URLs, and the total number of messages for
the same time periods.” Torres Depo. Tr. at 27:20-23.

38

App. 2043

demographic data to website owners, or used as part of a recommendation. Individual
enquiry is necessary to make these determinations.
89.

The second set of calculations surrounds the alleged presence of 15.9 billion friendship
ties on the Social Graph.93 These data come from May 2011. Mr. Torres states, “I would
estimate the value of the enhancement to the Social Graph as commensurate with the
ratio of (1) intercepted URLs in private messages during the Class period to (2) the total
number of links on the Social Graph.” However, these second set of calculations does not
make sense as a denominator in Mr. Torres’s proposed ratio for two reasons. First, the
value of friendship ties that are used to target advertising is completely distinct from
aggregate URL counts, which are not used to refine targeted advertising. Second, even
supposing the aggregate link counts were used to produce advertising revenue, which
they were not, the number of friendship ties would be the wrong denominator. The
correct denominator, which would be orders of magnitude larger, would include not just
friendship ties but every interaction between friends on Facebook—every “Like,” every
“share,” every piece of demographic information, and the content of every public posting.
Further complicating the analysis, each of these different drivers of the potential for
Facebook to generate advertising revenues have different efficacy in different
circumstances and at different times.94

90.

The third set of calculations surrounds the value of the Social Graph. However, Mr.
Torres has also overestimated the value of the Social Graph for at least five reasons.

91.

First, there is an error in the calculation of the value of the Social Graph. The average
quarterly revenue Mr. Torres based his estimate on was total revenue, not advertising
revenue.95 This means that the estimates also include revenues from Facebook’s activities

93
94

95

Torres Report ¶ 49.
See, e.g., Tucker, Catherine E., “Social Networks, Personalized Advertising, and Privacy Controls,” Journal of
Marketing Research, Vol. 51, No. 5, 2014, pp. 546-562, where I show that different undergraduate institutions
have different values for advertisers, as does the rarity of information - for example, liking Oprah Winfrey may
be less informative than liking an obscure 1970s poet.
Looking at slide 9, 2015 Q2 Results PowerPoint, the average of total revenues over the past four quarters equals
the $1,771 figure noted by Mr. Torres in footnote 66 of his report. The average of total advertising revenues
could potentially be estimated from Slide 10 of the same document, at $1,622.25.

39

App. 2044

including payments in online games.96 Correcting this error (which Mr. Torres
acknowledged in his deposition) reduces Mr. Torres’s estimate of the Social Graph’s
value by $1.267 billion dollars.97
92.

Second, the choice of revenue numbers appears selective and problematic. The equations
in Mr. Torres report suggest that the change in value was contemporaneous with the
alleged practice, suggesting the use of revenue from the span of years governed by the
class definition. However, the Torres report instead uses just the most recent four quarters
in 2014 and 2015 as a basis for advertising revenue. Using the span of years covering the
class definition as a basis for average revenue, suggests a valuation of the Social Graph
that is $7 billion lower than the one suggested in the Torres report.98

93.

Third, Mr. Torres’s allocation of costs is as follows: “the additional information collected
through the accused activities has arguably zero incremental cost. Therefore, from an
economic perspective, virtually all of the incremental advertising revenue generated from
the enhancement can justifiably be considered incremental profit to Facebook.”99 This
seems arbitrary, as it is not clear from this description to what incremental part of

96

97

98

99

This led the estimates in Table 1 in his report to be off by $1.2 billion. This error was confirmed in his
deposition. Torres Depo. Tr. at 195:10-204:9.
Mr. Torres initially estimated the value of the Social Graph to be $15.087 billion. Torres Report ¶ 43, Table 1.
In his deposition, he stated that he intended the valuation to be $13.820 billion. Torres Depo. Tr. at 204:4-9 (“Q.
So, those three corrections on page 15, is that all, Mr. Torres? A. Yes. And then that feeds into the table 1,
where the annual profit numbers would be 3,459,000,000, and the discounted values in that line, for the whole
line, for the full column, would be 2915, 2457, 2070, 1745, 1470, 1239, 1044, and 880, for a total of
13,820,000,000.”). I understand that Mr. Torres has made corrections to the report to rectify this error but these
corrections were submitted too close to the deadline for the submission of my report for me to be able to review
them.
The actual amount of the overstatement is $7.056 billion ($7.056 billion = $15.087 billion - $8.031 billion (see
Exhibit HHH)). While it does not affect his estimate of the value of the Social Graph, Mr. Torres made yet
another error related to his revenue estimate. He claims his revenue estimate is based on “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.” Torres Report ¶ 39 note 66). The period April 2014 to June 2015, however, contains five
quarters, not four. A review of his calculations, after taking into account the $1.267 billion error identified
above, indicates Mr. Torres is using quarterly advertising revenue for the four quarters between July 2014 and
June 2015. Torres Report ¶ 39, n. 66, and Facebook, Inc.’s 2015 Q2 Earnings Report (July 29, 2015), slide 10.
Torres Report ¶ 44.

40

App. 2045

Facebook’s revenue-generating functions Mr. Torres thinks Facebook’s considerable
costs should be allocated.100
94.

Fourth, Mr. Torres excludes research and development costs from Facebook’s expenses
when calculating Facebook’s profit margin. His argument is that expenditures for
research and development are intended to yield benefits in the future and are therefore not
appropriate to be accounted for today to determine current period profits. Mr. Torres
claims this is consistent with “accepted valuation standards.”101 However, though Mr.
Torres is correct that valuation practitioners often exclude current period research and
development from current period calculations of profit, they still include research and
development expenses from prior periods that are resulting in benefits today.102 In fact,
the text that Mr. Torres cites as the basis for his Income Valuation Approach103 includes
research and costs as an expense in a sample income valuation case study.104 Moreover,
Mr. Torres assumes that the benefit to Facebook related to the Social Graph will accrue
over eight years. In order for the Social Graph to remain a valuable asset to Facebook, it
will need to continue to invest in the Social Graph. To the extent that this has historically
required Facebook to invest in research and development to support and develop the
Social Graph, this need will continue into the future and through Mr. Torres’s eight-year
time horizon. By failing to account for research and development expenses, Mr. Torres is
biasing Facebook’s profit margin up, which then biases his estimate of Facebook’s
benefits up as well. Including research and development expenses for the years Mr.
Torres considered in his valuation as a proxy for historical research and development

100

101
102

103

104

Considerable costs as defined by Mr. Torres (cost of revenue, marketing and sales, and general and
administrative expenses) and outlined in Exhibit GGG have ranged from 35 percent (Q2’14) to 103 percent
(Q2’12) as a percentage of revenue. Torres Report ¶ 39 and Exhibit 1.
Torres Report ¶ 39, note 67.
See, e.g., Damodaran, Aswath, “Research and Development Expenses: Implications for Profitability
Measurement and Valuation,” NYU Stern School of Business,
http://people.stern nyu.edu/adamodar/pdfiles/papers/R&D.pdf, in which he argues that research and
development expenses should be capitalized and amortized as opposed to being charged to the quarter in which
they are incurred. Importantly, in both positions it is assumed that research and development costs will be
accounted for somewhere in the valuation.
See Torres Report note 63 in which he cites Smith, G.V. and R.L. Parr, Valuation of Intellectual Property and
Intangible Assets, John Wiley & Sons, 2000; Reilly, R. F. and R.P. Schweihs, Valuing Intangible Assets,
McGraw Hill, 1999. Mr. Torres also cites Smith and Parr in footnotes 64 and 96.
See Smith and Parr, Table 18.3 on pages 510 and 511.

41

App. 2046

expenses would reduce the Social Graph valuation by over $7 billion down from the
number presented in the report.105 In combination with the correction to the selectivity of
the years used, the Social Graph valuation would drop from the $15 billion figure stated
by $10 billion.106
95.

Fifth, at a more conceptual level, Mr. Torres’s decision to give the Social Graph a
lifetime of eight years based on geographical mobility misses a critical fact: The nature of
Internet advertising makes geography not that relevant as a targeting variable relative to
friendship ties or expressed interests.107 Furthermore, the history of social networks has
shown the vulnerability of any social network site to turmoil and displacement and users
leaving the site.108 For example, it would have been wrong to assume that the Social
Graph embedded in MySpace in 2008 would have a lifetime value of eight years, given
that within less than a year its users had left the site in droves.109 Mr. Torres was in fact
posed with this hypothetical in his deposition and stated that in order to value the
MySpace Social Graph he would have to “perform a series of due diligence and
preliminary analyses.”110

105

106

107

108

109

110

The actual amount of the overstatement is $7.456 billion ($7.456 billion = $15.087 billion - $7.631 billion (see
Exhibit III)).
The actual amount of the overstatement is $10.704 billion ($10.704 billion = $15.087 billion - $4.383 billion
(see Exhibit JJJ)).
Indeed, my own research emphasizes that geography becomes meaningful as a targeting variable only when
offline advertising channels are not available to the advertiser. See Goldfarb, Avi and Catherine Tucker,
“Advertising bans and the substitutability of online and offline advertising,” Journal of Marketing Research
48.2 (2011): 207-227.
Tucker, Catherine, and Alexander Marthews, “Social Networks, Advertising, and Antitrust,” George Mason
Law Review, Vol. 19, 2012, pp. 1211-1227.
Torkjazi, Mojtaba, Reza Rejaie, and Walter Willinger, “Hot Today, Gone Tomorrow: On the Migration of
MySpace Users,” Proceedings of the 2nd ACM Workshop on Online Social Networks, 2009.
Torres Depo. Tr. at 211:21-212:5 (“Q. If you were tasked with valuing the social graph of Myspace in 2007,
would you have used a similar methodology as one that you’ve used here? A. Well, in that hypothetical
situation, I would have to, to perform a series of due diligence and preliminary analyses. I’m not sure that
Myspace had the same revenue mode, so I would have to reconsider the revenue model then, and, to see if that
is sufficient.”).

42

App. 2047

C.

It is not clear how the proposed methodology related to allegedly inflated
social plugin counters is linked to the disputed practice
1.

96.

Summary of Mr. Torres’s method for estimating the alleged benefit to
Facebook related to allegedly “inflated” social plugin counters

Mr. Torres’s second proposed analysis, which is related to the “Like” button next to a
social plugin counter, describes two potential bounds for damages related to each URL
attachment created.111 The first is to try and establish how much the website owner might
benefit from additional “Likes.” The second is to establish the market value of these
“Likes” in order to determine what website owners would have needed to pay in order to
acquire the “Likes.” However, both of these proposed methodologies are unrelated to the
claims made by Plaintiffs over the harm they suffered and seem to misunderstand the
reasons why website owners value “Likes.”
2.

97.

The analysis focuses on the value of “Likes” to website owners, which has
no reliable link to Plaintiffs’ allegations of harm

Mr. Torres’s methodology for estimating the benefits from inflating the social plugin
counter on third-party websites attempts to quantify the amount of money that third-party
website owners either received from the allegedly inflated “Likes” or would have been
willing to pay to acquire the allegedly inflated “Likes.” Even if Mr. Torres were to
measure these amounts accurately the benefit to the subset of third-party website owners
willing to pay for Likes are not benefits received by Facebook.

98.

Mr. Torres suggests that “In the Facebook environment, the number of ‘Likes’ measured
is typically interpreted as an indicator of the reach of an advertising strategy and, given
the particular brand/product combination, as a factor in generating sales.”112 However,
since “Likes” incremented were never used on the Facebook advertising platform to
measure the reach or success of a Facebook advertising strategy, this analogy is
misguided. Mr. Torres then attempts to link the benefit to third-party website owners to
Facebook by claiming that

111
112

Torres Report ¶¶ 62-71.
Torres Report ¶ 64.

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App. 2048

“The amounts identified in this analysis – the cost savings to advertisers
from the accrual of Likes from the intercepted messages – were, in
principle, made available to spend on additional Facebook marketing
campaigns. This would have been particularly true in light of the false
appearance of increase [sic] Fan engagement that an inflated [social
plugin] count would present. To that extent, a fraction of this benefit may
have been converted to advertising revenue benefiting Facebook.”113
99.

The link is tenuous. Mr. Torres provides no method for determining if the cost savings
were actually spent on Facebook advertising or, if so, how much was spent. He does not
even argue with certainty that any of it resulted in incremental revenue to Facebook, just
that “in principle” it was available to be spent on Facebook marketing and that it “may
have been converted.” In his deposition, Mr. Torres confirmed only “a fraction [of an
advertiser’s cost savings] would have been converted,”114 to Facebook revenue, but was
unable to state what fraction, stating, “I can’t tell you because I don’t have the
information to determine it.”115

100.

Instead, the argument in Mr. Torres’s Report is “this practice gave its clients, Marketers,
an incremental impression of effectiveness of their Facebook marketing campaigns.
Marketers perceiving an incremental return of their spending on Facebook campaigns
were undoubtedly encouraged to allocate additional funds to these campaigns.”116 The
argument is that when a third-party website observed an increase in a social plugin
counter, they diverted the funds that they would have spent on incrementing the social
plugin counter towards Facebook advertising. However, this argument is flawed for at
least four reasons.

101.

First, as discussed above, many third-party websites do not have social plugin counters.
Second, among those third-party websites that have social plugin counters many do not
pay to advertise on Facebook. Indeed, much advice on social media emphasizes the

113
114

115

116

Torres Report ¶ 73.
Torres Depo. Tr. at 295:6-13 (“Q. And does your report assume that advertisers would have passed 100 percent
of their cost savings on to Facebook? A. Is that my assumption, that they would – Q. Yes. Is that your
assumption? A. No. Q. What is your assumption, then? A. That a fraction would have been converted.”).
Torres Depo. Tr. at 295:14-22 (“Q. Which fraction? A. I don’t have the information to determine that fraction.
W. Can you tell me it’s more than 50 percent? A. I can’t tell you, because I don’t’ have the information to
determine it.”).
Torres Report ¶ 68.

44

App. 2049

extent to which it is desirable often to not spend money on advertising.117 In his
deposition, Mr. Torres agreed that his definition of “Marketers” means that the focus is
on third-party websites who purchase advertising.118 However, the class definition
includes many URL messages where the website did not and would not spend money on
advertising on Facebook. Indeed, some examples from Named Plaintiff Mr. Hurley
.119 Similarly, Mr. Campbell shared URLs for
.120
.121
102.

Second, the mechanism by which Facebook allegedly benefited may in fact have had the
opposite effect. Mr. Torres argues that Marketers would have concluded that Facebook
marketing was more effective because of the incremental “Like” and devoted more
money to Facebook advertising.122 If the social plugin counter incremented without any
extra effort or expenditure on advertising from the firm itself, the firm may take this as
suggestive that its organic (or non-paid) marketing efforts were successful and be less
likely to divert money to advertising.

103.

Third, there are many reasons to think that website owners understood the varied
providence of “Likes” displayed on the social plugin counter, especially given that the

117

118

119

120

121

122

Edelman, David, and Brian Salsberg, “Beyond Paid Media: Marketing’s New Vocabulary,”
McKinsey&Company, November 2010,
http://www mckinsey.com/insights/marketing_sales/beyond_paid_media_marketings_new_vocabulary, viewed
January 11, 2016.
Torres Depo. Tr. at 98:2-8 (“Q. What do you mean by, marketers? A. In this report, I mean by marketers the
same thing that Facebook defines as marketers, which are their clients, the people responsible for advertising,
companies, entities, organizations, and whether they are direct entities or agencies in the advertising market.”).
See HURLEY000001 where the URL
was shared for
example.
See Plaintiff Matthew Campbell’s Corrected Objections and Responses to Defendant Facebook, Inc.’s First Set
of Interrogatories.
For example, the IRS itself imposes a long list of restrictions on potential advertisements that anyone connected
with the IRS can use. See “Advertising Standards,” IRS, last updated 07-Jan-2016,
https://www.irs.gov/uac/Advertising-Standards, viewed January 15, 2016.
Torres Report ¶¶ 68, 73.

45

App. 2050

instructions for installing the counter explicitly stated it would include “Likes” created
from URL attachments.123
104.

Without a bridge between the alleged “benefit” received by third-party website owner
and any alleged “benefit” to Facebook, Mr. Torres’s damage theory for the allegedly
inflated social plugin counter is divorced from the way that Plaintiffs described the harm
they suffered.
3.

The analysis fundamentally misunderstands or distorts why website
owners value “Likes”
a.

105.

The analysis focuses on the value of “Likes” that allowed a
continuing relationship between the website and an individual
rather than social plugin counters

By themselves “Likes” have little value to third-party websites. Recent research broadly
contradicts Mr. Torres’s assertion that “Likes can be profitable.”124 Harvard researchers
found in multiple experiments that “Liking” a brand has no effect on subsequent
consumer attitudes or behavior, including advertisement choice and actual purchase.125
Indeed, it appears likely that the study that Mr. Torres cites in Table 3 of his report126 does
not actually represent anything profitable that is causally connected with a “Like.” This
table compares the cost of inducing a “conversion” between a “Fan” and a non-”Fan” for
a variety of products.127 However, people who have a greater tendency to become a Fan of
a product are also easier to convert irrespective of whether they click a “Like” button.
There is no causal relationship implied by this data or profitability that can be attributed
to the “Like” button.

123

124
125

126
127

See FB000000163 from March 2011 (captured by the Wayback Machine) for an example of the text available
on Facebook’s developer website. The text explicitly says that the count includes “Likes” deriving from the
creation of URL attachments in messages. See also FB000000166 from October 2012 (also captured by the
Wayback Machine) with similar information.
Torres Report ¶ 70.
John, Leslie et al., “What are Facebook ‘Likes’ Really Worth?,” HBS Working Paper, 2015,
http://rady.ucsd.edu/docs/events/lesliejohn.pdf. This is also illustrated by the wide variety of motivations for
“Liking”, such as the desire to receive a discount or an offer, displayed in Table 1.
Torres Report at 26.
Note that this “Fan” language represents an earlier incarnation of Facebook, where users could be “Fans” of,
rather than “Like” an organization, so it is not quite certain how relevant it is for an analysis of “Likes” in any
case.

46

App. 2051

106.

Instead, the “value” of a “Like” to a third-party website or to a Facebook page is that it
enables that organization to form a relationship with that user and share communications
with them. Indeed, research shows128 that the only value of “Likes” to advertisers is that
they allow the user to subscribe to the conventional marketing communications put out
by that advertiser’s main Facebook page. This implies that the kind of “Like” that is an
anonymous increment of a social plugin counter, and that does not allow a website to
form a relationship with the user, has little worth. Therefore, trying to ascribe value to all
“Likes” based on valuations of “Likes” that allowed or implied a continuing relationship
between the organization and an individual is misguided.

107.

In general, Mr. Torres’s Report fails to distinguish between users actually clicking on
“Like” buttons on third-party websites with changes in the display of counters on those
third-party websites. For example, Mr. Torres cites an internal Facebook email chain for
the proposition that “from [the Like button’s] launch in April 2010, the impact of social
plugins was significant, generating 815 million clicks on ‘Like’ buttons daily in the first
few weeks.”129 However, the document indicates that Facebook’s partners had a wide
range of outcomes with respect to implementing social plugins – which are themselves
broader than a social plugin counter. For example, traffic on the Rotten Tomatoes movie
reviews website actually fell after implementing social plugins, suggesting that any
effects are not straightforward or uniform.130 Similarly, the document Mr. Torres uses to
demonstrate “Benefits of Using Like Button Plugins” conflates the potential for
anonymous incrementing of the social plugin counter with users clicking the “Like”
button.131

108.

Given this, any attempt to use a valuation for a “Like” that might include a meaningful
and ongoing relationship between the website and website user is wrong.

128

Mochon, Daniel, Karen Johnson, Janet Schwartz, and Dan Ariely, “How much is a like worth? A field
experiment of Facebook pages,” Tulane University Working Paper – Advances in Consumer Research, vol. 42,
2015. This paper is under the review process so is not publicly available.

129

Torres Report ¶ 29.
“Partners: social plugins,” Internal Facebook Email Chain, FB000011715.
“Connecting Outside of Facebook,” PowerPoint Presentation at Slide 4, FB000026793.

130
131

47

App. 2052

109.

Mr. Torres argues that “the average cost of advertising on Facebook to encourage a user
to become a Fan – Like the advertiser’s Facebook page – was $1.07. This cost also varies
across sectors and over time. In 2012, the cost per acquired Fan (i.e., cost per click in Fan
acquisition campaigns) averaged $0.55.”132

110.

There are four things to note about these estimates. First, they refer to “Fans,” not
“Likes.” Second, they refer to a situation where an organization will subsequently, as a
result of the Fan relationship, be able to communicate with that audience via the
Facebook platform and so do not reflect the market value of an anonymized +1 increase
in a plugin counter on a third-party website. Third, these estimates themselves show the
huge variability in potential estimates of the costs of obtaining a “Like” (which again, is
distinguishable from the anonymous incrementation at issue here). Indeed, there are
estimates that suggest a cost of obtaining a “Like” can via Facebook advertising is
$0.08.133 Estimates which range, depending on the study used, from $0.08 to $1.07 are not
a reliable guide for damages. Fourth, as shown in the earlier example of the promotion of
the BostonEventsInsider website shown in Figure 6, there are many other ways of
incentivizing users to give “Likes” which might even be cheaper than paying for them –
in that particular case, the website had not paid money for the movie tickets it was using
to incentivize customers to “Like” their website.

111.

It might be supposed that the estimates of “phony” purchases of “Likes” cited by Mr.
Torres, such as the case where “Likes” were sold for $0.075, are therefore more
relevant.134 However, there are at least two issues with such numbers. First, “Likes” are
often actually cheaper than the article cited.135 One website test suggests that “Likes” can

132
133

134

135

Torres Report ¶ 70.
Chieruzzi, Massimo, “Buying Facebook Likes Sucks, Here’s The Data To Prove It!,” AdEspresso, November
19, 2014, https://adespresso.com/academy/blog/buy-facebook-likes/, viewed December 12, 2015.
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-yousell-you-1-000-facebook-likes, viewed December 12, 2015.
For example, http://www.buylikesandfollowers net/buy-facebook-likes-cheap html suggests that it would cost
$0.03 a “Like” if you buy 10,000 “Likes.” “Buy Real Facebook Likes,” Buylikesandfollowers.net,
http://www.buylikesandfollowers.net/buy-facebook-likes-cheap html, viewed December 12, 2015.

48

App. 2053

be bought as cheaply as $0.01. 136 Second, the market price of such “Likes” may reflect
the potential belief among buyers (whether true or not) that “Likes” might actually
translate into real people taking real actions. As such the price would be higher than for
an anonymous increment of the social plugin counter where there was definitely not such
a possibility.
b.

112.

The Proposed Methodology For Social Plugin Counters Does
Not Address The Fact That Many Proposed Class Members
Were Unaffected Or Benefited From These Practices.

Mr. Torres’s proposed methodology does not distinguish between the many cases where
the user was unaffected as there was no counter or social plug-in that displayed counts.
Indeed, it seems to presume the presence of a social plugin counter on the website for
every message where an attachment was created. However, many websites do not have
social plugins and many social plugins do not provide a counter.137

113.

Mr. Torres’s proposed methodology also does not consider the cases where a user was
invested in the website, meaning they would have welcomed or benefited from the
potential for an increment of the social plugin counter, supposing the website did indeed
have a plugin that contained the counter.
D.

114.

Mr. Torres’s two potential methodologies cannot be reconciled with each
other

Last, these two separate proposed methodologies cannot be reconciled with the different
claims that proposed class members may have. In particular, it is not clear how the
proposed methodology would avoid double-counting the benefits in instances where a
message contained a URL during the period that such a share could have potentially
incremented a social plugin displaying a counter. Mr. Torres has two competing
suggestions for how to resolve this issue.

115.

First, in his Report, Mr. Torres suggests: “the calculated effect from incremental
advertising revenue during the time when the Like counters were being affected (through

136

137

Chieruzzi, Massimo, “Buying Facebook Likes Sucks, Here’s The Data To Prove It!,” AdEspresso, November
19, 2014, https://adespresso.com/academy/blog/buy-facebook-likes/, viewed December 12, 2015.
Declaration of Alex Himel ¶¶ 34-35, 37.

49

App. 2054

December 2012) . . . shall be deducted from the benefits calculated for this period under
the methodology described in the previous section [the Social Graph method] for affected
Class Members.”138
116.

This proposal leads to conflicts in the interests of different putative class members. The
following thought experiment provides an example of possible conflicts, taking as given
that these methodologies are capable of producing concrete numbers and that the
numbers would be relevant.

117.

Suppose that between 2011 and 2015, 50 million URLs in messages were affected.
Suppose that in the first year of this period (2011-2012), 10 million URL messages were
affected. Suppose that the Social Graph method produced a calculation of 1 cent per
message-URL. Suppose also that the “Like”-counter valuation method produced a value
of five cents per message-URL in the 2011-2012 period. Under Mr. Torres’s Social
Graph method, the available damages to be split among class members would be
$500,000. Under the “Like”-counter valuation method, the available damages to be split
among affected class members would also be $500,000. However, under the
reconciliation proposal in Mr. Torres’s Report, that “Like”-counter total of $500,000
would need to be subtracted from the Social Graph method total of $500,000, implying
zero dollars available for any class members who sent messages containing URLs after
December 2012. Now that might be correct, given the negligible effects of the URL
counts after December 2012, but it does suggest a conflict of interest of the proposed
class members inherent in the two methodologies. Any proposed class member who sent
messages mainly prior to December 2012 would have an interest in maximizing the value
calculated by the “Like”-counter valuation method; any proposed class member who only
sent messages after December 2012 would prefer that the “Like”-counter valuation
method provided very low valuations.

118.

Second, in his deposition, Mr. Torres testified that ultimately his goal was to make sure
the overlap was taken into account and that “when everything is said and done . . . only

138

Torres Report ¶ 74.

50

App. 2055

one of the two calculations will prevail.”139 In response to a thought experiment similar to
the one in the previous paragraph, he said that ultimately, “you wouldn’t add them
together. You would just have one.”140 Similarly, Mr. Torres made clear that his
methodology could not give rise to a negative number because “if the overlap
overwhelms the situation, then only one of [the figures] would be appropriate.”141
119.

Mr. Torres’s suggested solution during his deposition is fundamentally different than the
solution proposed in his Report. Therefore, it is unclear how Mr. Torres would actually
reconcile his competing damages methodologies. Further, his testimony suggests that he
thinks that only one set of putative class members may recover and therefore, the
conflicts in the interests of different Class Members remain unresolved.
E.

120.

Rebuttal to Mr. Torres’s analysis as it pertains to statutory damages

I understand that the Court has discretion regarding whether to award statutory damages
and, if so, the amount. I also understand that the Court may consider several factors in
this determination including, among others, the actual damage to the victim and whether
the Defendant profited from the alleged violation. I have no opinion regarding whether
statutory damages are appropriate or not, but I note where my analysis and rebuttal to the
Torres report addresses these two factors. Mr. Torres explicitly stated in his deposition he
was not offering an opinion relating to statutory damages, so I emphasize that these are

139

140

141

Torres Depo. Tr. at 300:3-19 (“Q. But how would the net, if you are saying that you would deduct the amounts,
the analysis in this section shall be deducted from the benefits calculated under the methods described in the
previous section, okay, I’m saying, if the benefits were greater than the calculated – A. Now, what this means is
that . . . what this means is that the overlap has to be taken into account. That overlap can be calculated, when
everything is said and done, and that overlap means that only one of the two calculations will prevail. Q. One of
the two, meaning A or B? A. So, if you add A and B, you would then have to take away the overlap.”).
Torres Depo. Tr. at 299:4-8 (“So, if it were to be the case that benefits from one perspective are the same as the
benefits from the other perspective, then, yeah, the overlap with, would mean that you wouldn’t add them
together. You would just have one.”).
Torres Depo. Tr. at 299:10-23 (“Q. And what if the benefits were greater than the calculated effect from the
incremental advertising revenue? That would result in a negative number? A. In, it would be a very strange
hypothetical situation where that would even be the case, because of the length of the time period. Q. But, if it
were the case, it would be a negative number? A. So, whatever the methodology determines for those two
numbers would have to do the analysis of the overlap, and, if the overlap overwhelms the situation, then only
one of them would be appropriate.”).

51

App. 2056

App. 2057

App. 2058

EXHIBIT DDD

App. 2059

C ATHERINE T UCKER
MIT Sloan School of Management
100 Main St, E62-536
Cambridge MA 02142

Tel: [phone]
[e-mail]
http://cetucker.scripts.mit.edu

E DUCATION
Stanford University, Ph.D. in Economics (Advisor: Tim Bresnahan), 2005
Oxford University, BA in Politics, Philosophy and Economics, 1999

A PPOINTMENTS
MIT Sloan, Sloan Distinguished Professor of Management Science, September 2015 –
MIT Sloan, Chair MIT Sloan PhD Program, July 2015 –
MIT Sloan, Professor of Management Science, July 2015–
National Bureau of Economic Research (NBER), Research Associate, September 2012 –
MIT Sloan, Mark Hyman Jr. Career Development Professor (with tenure), July 2012 –
MIT Sloan, Associate Professor of Management Science, July 2011 –
National Bureau of Economic Research (NBER), Faculty Research Fellow, May 2011 –
September 2012
MIT Sloan, Douglas Drane Career Development Chair in IT and Management, July 2006 –
MIT Sloan, Assistant Professor of Marketing, July 2005 – June 2011

App. 2060

H ONORS

AND

AWARDS

2015

Erin Anderson Award

2014

Paul E. Green Award

2013

Teacher of the Year Award, MIT Sloan

2013

Jamieson Prize for Excellence in Teaching

2012

Garfield Economic Impact Award for Best Paper in Health Economics

2012

Nominated for Teacher of the Year award (Also in 2010 and 2009)

2011

WHITE Award for best paper in the Economics of Healthcare IT

2011

Public Utility Research Prize for the best paper in regulatory economics

2011

NSF CAREER Award

2011

MSI Young Scholar

2010

Management Science Distinguished Service Award

2004

Koret Foundation Scholar, Stanford Institute for Economic Policy Research Fellowship

2004

Fourth Annual Claire and Ralph Landau Student Working Paper prize

P UBLISHED /A CCEPTED PAPERS
1. ‘Identifying Formal and Informal Influence in Technology Adoption with Network
Externalities’, Management Science, Vol. 55 No. 12, December 2008, pp. 2024-2039
2. ‘Privacy Protection and Technology Diffusion: The Case of Electronic Medical Records’
with Amalia Miller, Management Science (Lead Article), Vol. 55 No. 7, July 2009, pp.
1077-1093
• Republished as part of Informs ‘Healthcare in the Age of Analytics’ series
3. ‘How Sales Taxes Affect Customer and Firm Behavior: The Role of Search on the
Internet’ with Eric Anderson, Nathan Fong and Duncan Simester, Journal of
Marketing Research, Vol. 47 No. 2, April 2010, pp. 229-239
4. ‘Growing Two-sided Networks by Advertising the User Base: A Field Experiment’, with
Juanjuan Zhang, Marketing Science, Vol. 29 No. 5, September-October 2010, pp.
805-814
5. ‘Privacy Regulation and Online Advertising’ with Avi Goldfarb, Management Science,
Vol. 57 No. 1, January 2011, pp. 57-71
6. ‘Search Engine Advertising: Channel Substitution when Pricing Ads to Context’, with
Avi Goldfarb, Management Science, Vol. 57 No 3, March 2011, pp. 458-470

App. 2061

7. ‘Stuck in the Adoption Funnel: The Effect of Interruptions in the Adoption Process on
Usage’ with Anja Lambrecht and Katja Seim, Marketing Science, Vol. 30 No. 2,
March-April 2011, pp. 355-36
8. ‘Advertising Bans and the Substitutability of Online and Offline Advertising’, with Avi
Goldfarb, Journal of Marketing Research (Lead Article), Vol. 48 No. 2, April 2011, pp.
207-227
9. ‘Can Healthcare Information Technology Save Babies?’ with Amalia Miller, Journal of
Political Economy, Vol. 119 No. 2, April 2011, pp. 289-324
10. ‘How Does Popularity Information Affect Choices? A Field Experiment’ with Juanjuan
Zhang, Management Science, Vol. 57 No. 5, May 2011, pp. 828-842
11. ‘Online Display Advertising: Targeting and Obtrusiveness’ with Avi Goldfarb,
Marketing Science (Lead Article and Discussion Paper), Vol. 30 No. 3, May-June 2011,
pp. 389-404
• ‘Rejoinder - Implications of "Online Display Advertising: Targeting and
Obtrusiveness’ with Avi Goldfarb, Marketing Science, Vol. 30 No. 3, May-June
2011, pp. 413-415
• Nominated for John D. C. Little Award
12. ‘Encryption and Data Security’ with Amalia Miller, Journal of Policy Analysis and
Management, Vol. 30 No. 3, Summer 2011, pp. 534-556
13. ‘Paying With Money or With Effort: Pricing When Customers Anticipate Hassle’ with
Anja Lambrecht, Journal of Marketing Research, Vol. 49 No. 1, February 2012, pp.
66-82.
14. ‘Heterogeneity and the Dynamics of Technology Adoption’ with Stephen Ryan,
Quantitative Marketing and Economics, Vol 10 No. 1, March 2012, pp 63-109
15. ‘Shifts in Privacy Concerns’, American Economic Review: Papers and Proceedings with
Avi Goldfarb, Vol. 102 No. 3, May 2012, pp. 349-53
16. ‘How does the Use of Trademarks by Intermediaries Affect Online Search?’ with Lesley
Chiou. Marketing Science, Vol 31 No. 5, September 2012, pp 819-837
17. ‘Active Social Media Management: The Case of Health Care’ with Amalia Miller.
Information Systems Research Vol. 24, No. 1, March 2013, pp. 52-70
• Republished as part of Informs ‘Healthcare in the Age of Analytics’ series

App. 2062

18. ‘Paywalls and the Demand for News’ with Lesley Chiou. Information Economics and
Policy Volume 25 No. 2, June 2013, pp. 61-69
19. ‘Days on Market and Home Sales’ with Juanjuan Zhang and Ting Zhu. RAND Journal
of Economics Volume 44 No. 2, pages 337-360, Summer 2013
20. ‘When Does Retargeting Work? Timing Information Specificity’ with Anja Lambrecht.
Journal of Marketing Research (Lead Article) Vol. 50 No. 5, October 2013, pp. 561-576
• Paul E. Green Award for the ‘Best article in the Journal of Marketing Research
that demonstrates the greatest potential to contribute significantly to the practice
of marketing research.’
21. ‘Health Information Exchange, System Size and Information Silos’ with Amalia Miller.
Journal of Health Economics, Vol. 33 No. 2, January 2014: pp. 28-42
22. ‘Electronic Discovery and the Adoption of Information Technology’ with Amalia Miller.
Journal of Law, Economics, & Organization (Lead Article), Vol. 30. No. 2, May 2014,
pp. 217-243
23. ‘Social Networks, Personalized Advertising, and Privacy Controls.’, Journal of
Marketing Research, Vol. 51, No. 5, October 2014, pp. 546-562.
24. ‘Trademarks, Triggers, and Online Search’ with Stefan Bechtold. Journal of Empirical
Legal Studies Vol. 11 No. 4, December 2014
25. ‘The Reach and Persuasiveness of Viral Video Ads’ Marketing Science Vol. 34, No. 2
2015 pp. 281-296
26. ‘Privacy Regulation and Market Structure’ with James Campbell and Avi Goldfarb.
Journal of Economics & Management Strategy Vol 24, No. 1, Spring 2015, pp 47-73
27. ‘Standardization and the Effectiveness of Online Advertising’ with Avi Goldfarb.
Management Science Vol 61, No. 11, 2015, pp 2707-2719
28. ‘Harbingers of Failure’ with Eric Anderson, Song Lin and Duncan Simester. Journal of
Marketing Research (Lead Article) Oct 2015, Vol. 52, No. 5, pp. 580-592.
29. ‘The Effect of Patent Litigation and Patent Assertion Entities on Entrepreneurial
Activity’. Research Policy Vol 45, No. 1, February 2016, Pages 218-231

App. 2063

C HAPTERS

IN

E DITED V OLUMES

AND

S UMMARY P IECES

30. ‘Modeling Social Interactions: Identification, Empirical Methods and Policy
Implications’ with Wes Hartmann, Puneet Manchanda, Harikesh Nair, Matt Bothner,
Peter Dodds, David Godes and Karthik Hosanagar, Marketing Letters, Vol. 19 No. 3,
December 2008, pp. 287-304
31. ‘Search Engine Advertising - Examining a profitable side of the long tail of advertising
that is not possible under the traditional broadcast advertising model’ with Avi
Goldfarb, Communications of the ACM, Vol. 51 No. 11, November 2008, pp. 22-24
32. ‘Online Advertising’, with Avi Goldfarb, Advances in Computers, Vol. 81, March 2011,
Marvin Zelkowitz (Ed), Elsevier
33. ‘Substitution between Online and Offline Advertising Markets’, with Avi Goldfarb,
Journal of Competition Law and Economics, Vol. 7 No. 1, March 2011, pp. 37-44
34. ‘Online Advertising, Behavioral Targeting, and Privacy’, with Avi Goldfarb,
Communications of the ACM, Vol. 54 No. 5, May 2011, 25-27
35. ‘Privacy and Innovation’, Innovation Policy and the Economy, Vol. 11, 2012, Josh
Lerner and Scott Stern (Eds), NBER
36. ‘The Economics of Advertising and Privacy’, International Journal of Industrial
Organization, Vol. 30 No. 3, May 2012, pp. 326-329
37. ‘Empirical Research on the Economic Effects of Privacy Regulation’. Journal on
Telecommunications and High Technology Law, Vol. 10 No. 2, Summer 2012, pp.
265-272
38. ‘Social Networks, Advertising and Antitrust’, with Alex Marthews, George Mason Law
Review, 2012, Vol 19 No 5., pp. 1211-1227.
39. ‘Why Managing Customer Privacy Can Be an Opportunity’ with Avi Goldfarb, Spring
2013, Sloan Management Review
40. ‘The Implications of Improved Attribution and Measurability for Antitrust and Privacy
in Online Advertising Markets’, George Mason Law Review, Vol. 2 No. 2, pp. 1025-1054
(2013).
41. ‘Privacy and the Internet’ Chapter 11, Handbook of Media Economics, Forthcoming

App. 2064

42. ‘Field Experiments in Marketing,’ with Anja Lambrecht, Handbook of Marketing
Analytics, Forthcoming

P OLICY W RITING
43. OECD Roundtable on Privacy, Report on the ‘Economic Value of Online Information’,
December 2010
44. Written Congressional Testimony on ‘Internet Privacy: The Impact and Burden of
European Regulation,’ U.S. House Energy and Commerce Committee, September 2011

PAPERS

UNDER

R EVIEW

45. ‘How Do Restrictions on Advertising Affect Consumer Search?’ with Lesley Chiou.
Revise and resubmit at Management Science
46. ‘Digital Content Aggregation Platforms: The Case of the News Media.’ with Lesley
Chiou Revise and resubmit at RAND Journal of Economics
47. ‘Social Advertising’. Revise and resubmit at Management Science
48. ‘Patent Trolls and Technology Diffusion: The Case of Medical Imaging’ Revise and
resubmit at RAND Journal of Economics
49. ‘Should You Target Early Trend Propagators? Evidence from Twitter’ with Anja
Lambrecht and Caroline Wiertz. Revise and resubmit at Marketing Science
50. ‘Privacy Protection, Personalized Medicine and Genetic Testing’ with Amalia Miller.
Revise and resubmit at Management Science
51. ‘Government Surveillance and Internet Search Behavior’ with Alex Marthews Revise
and resubmit at Management Science
52. ‘Guns, Privacy and Crime’ with Alessandro Acquisti Revise and resubmit at
Information Systems Research
53. ‘Conducting Research with Quasi-Experiments: A Guide for Marketers’ with Avi
Goldfarb.

App. 2065

W ORK

IN

P ROGRESS

‘Spillovers from Product Failure’ with Amalia Miller
‘The Choice of Privacy Policy: The Case of Educational Software’ with Amalia Miller
‘Third-Party Certification: The Case of Medical Devices’ with Cristina Nistor
‘Big Bad Data: The Case of For-Profit College Advertising’ Avinash Gannamaneni and Avi
Goldfarb

I NVITED S EMINARS
Universities
1. June 2015, Marketing Group, University of Cambridge, UK
2. May 2015, Marketing Group, University of Texas at Dallas, TX
3. March 2015, Health Policy Group, Georgia State University, GA
4. March 2015, Marketing Group, University of Colorado, CO
5. February 2015, Strategy Group, University of North Carolina, NC
6. January 2015, Marketing Group, Emory University, GA
7. December 2014, OPIM, Wharton School of Management, PA
8. October 2014, Economics Department, Yale University, CT
9. September 2014, Marketing Group, Boston University, MA
10. March 2014, Technology Group, University of California at Berkeley, CA
11. January 2014, Marketing Department at Texas A&M
12. November 2013, Marketing Group, University of California at Berkeley, CA
13. October 2013, Marketing Group, Tulane University, LA
14. October 2013, Marketing Group, University of Houston, TX
15. May 2013, Tuck School of Management, Dartmouth University, NH
16. March 2013, Economics Department, University of Toulouse
17. March 2013, Marketing Group, Rotterdam University
18. March 2013, Economics Department, University of Zurich
19. March 2013, Marketing group, Georgia Tech
20. January 2013, Anderson School, UCLA
21. January 2013, Marketing Group, CMU
22. October 2012, Marketing Group, Stanford University
23. October 2012, Marketing Group, Columbia University
24. October 2012, Marketing Group, University of Texas at Austin
25. September 2012, Marketing Group, Harvard Business School
26. June 2012, Strategy Group, London Business School
27. March 2012, Marketing Group, Cornell
28. February 2012, IS Group, Indian School of Business
29. February 2012, Marketing Group, Wharton

App. 2066

30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.

61.
62.
63.
64.
65.
66.
67.

January 2012, Marketing Group, UCLA
November 2011, Marketing Group, University of Rochester
October 2011, Marketing Group, University of Zurich
October 2011, Department of Law and Economics, Swiss Federal Institute of Technology,
Zurich
May 2011, Marketing Group, National University of Singapore
May 2011, IS Group, National University of Singapore
May 2011, Strategy Group, LMU Munich
May 2011, Marketing Group, New York University
March 2011, Marketing Group, Florida University
February 2011, IS Group, New York University
November 2010, European School of Management and Technology
October 2010, Marketing Group, Yale University
October 2010, Networked Business Group, Harvard Business School
September 2010, TIES Group, MIT Sloan
July 2010, Department of Economics, University of Mannheim
March 2010, Marketing Group, Wharton School, University of Pennsylvania
January 2010, Marketing Group, University of Michigan
November 2009, Marketing Group, University of California at Berkeley
October 2009, Digital Business Seminar, MIT Sloan
December 2008, Marketing Group, MIT Sloan
November 2008, Marketing Group, Rady School of Business, UCSD
September 2008, Strategy Group, MIT Sloan
May 2008, Digital Strategy Group, Tuck School of Business, Dartmouth University
April 2008, Kellogg Management and Strategy Group, Northwestern University
March 2008, Marketing Group, Duke University
March 2008, Strategy Group, Chicago GSB
July 2007, Marketing Group, London Business School, London, UK
April 2007, Marketing Group, Chicago GSB
March 2007, Marketing Group, Rotman School, University of Toronto
November 2005, Economics Department, Harvard University
October 2004-February 2005 (Job Market): NYU Stern, University of Michigan,
University of Arizona, University of British Columbia, Federal Reserve Board, Federal
Reserve Bank of New York, Harvard Business School, Kellogg, MIT Sloan, Federal
Reserve Bank of Chicago, Stanford Economics Department
Other
April 2015, Federal Communications Commission
November 2014, Office of Research at the Consumer Financial Protection Bureau
April 2014, Big Data Working Group, The White House.
February 2014, Main Street Patent Coalition, Panel hosted at the Senate by Senator
Orrin Hatch
July 2013, Federal Communications Commission
August 2012, DG Competition, European Commission, Brussels
August 2012, Technology Policy Institute Conference, Aspen

App. 2067

68.
69.
70.
71.
72.

December 2011, Havas Digital, New York
June 2011, Eneca
September 2010, Federal Trade Commission
September 2010, Google European Public Policy Unit, Paris
July 2009, Information Technology and Innovation Foundation, Washington DC

P RESENTATIONS

OF

R ESEARCH

AT

C ONFERENCES

1. July 2015, NBER Law and Economics (co-author presented), Cambridge, MA
2. July 2015, NBER Economics of Digitization, Cambridge, MA
3. June 2015, ‘The Future of Research in the Digital Society’, French Ministry of Culture
˘¸
and Communication âAS Toulouse School of Economics, Paris, France
4. June 2015, Marketing Science, Baltimore, MD
5. June 2015, Doctoral Consortium, Baltimore, MD
6. March 2015, IP Leadership Conference, Washington, DC
7. February 2015, Patents in Theory and Practice, Washington, DC
8. June 2014, Marketing Science, Atlanta, GA
9. May 2014, Boston College Social Media Workshop, Boston, MA
10. January 2014, American Economic Association Meetings
11. July 2013, Marketing Science, Istanbul, Turkey
12. June 2013, Searle Center Conference on Internet Search and Innovation, Chicago, IL
13. April 2013, Brown University Mini-Networks Conference
14. February 2013, WSDM 2013 Conference (Keynote Speaker), Rome, Italy
15. January 2013, American Economic Association Meetings, San Diego, CA (Co-author
presented)
16. December 2012, New York Computer Science and Economics Day
17. November 2012, Search and Competition Conference, Melbourne Australia
18. October 2012, Economics of Personal Data, (Keynote Speaker), Amsterdam
19. August 2012, Amsterdam Symposium on Behavioral and Experimental Economics
20. July 2012, Fudan University Marketing Research Symposium, China
21. June 2012, Searle Center Conference on Internet Search and Innovation, Chicago, IL
22. June 2012, Innovation, Intellectual Property and Competition Policy Conference, Tilburg,
Netherlands
23. June 2012, Marketing Science, Boston, MA
24. June 2012, Social Media and Business Transformation, Baltimore, MD
25. May 2012, The Law and Economics of Search Engines and Online Advertising, George
Mason University, Arlington, VA
26. February 2012, NBER Economics of Digitization (co-author presented), Cambridge, MA
27. January 2012, Symposium on Antitrust and High-Tech Industries, George Mason
University, VA
28. January 2012, Patents, Standards and Innovation, Tucson, AZ
29. January 2012, Econometric Society Meetings, Chicago, IL
30. January 2012, AEA Meetings (2 papers), Chicago, IL

App. 2068

31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
66.
67.
68.

December 2011, Economics of Privacy Workshop, Boulder, CO
November 2011, Economics and Computation Day, Cambridge, MA
November 2011, HBS Strategy Research Conference, Boston, MA
November 2011, The Law and Economics of Internet Search and Online Advertising
Roundtable, George Mason University, Arlington, VA
November 2011, Patents Statistics for Decision Makers, Alexandria, VA
October 2011, Workshop on Health IT and Economics, Washington, DC
October 2011, Innovation, Organizations and Society, University of Chicago, IL
October 2011, Direct Marketing Research Summit, Boston, MA
September 2011, Invited Session ‘Economics and Marketing’, EARIE, Stockholm, Sweden.
July 2011, NBER Economics of Digitization, Cambridge, MA
July 2011, SICS, Berkeley, CA
June 2011, The Law and Economics of Search Engines and Online Advertising, George
Mason University, Arlington, VA
June 2011, Workshop on the Economics on Information Security, Washington, DC
June 2011, Marketing Science (3 papers), Houston, TX
June 2011, Searle Center Conference on Internet Search and Innovation, Chicago, IL
May 2011, Boston College Social Media Workshop, Boston, MA
May 2011, Technology Pricing Forum, Boston, MA
April 2011, NBER Innovation Policy and the Economy, Washington, DC
April 2011, International Industrial Organization Conference (3 papers), Boston, MA
March 2011, Technology Policy Institute, Washington, DC
February 2011, NBER Economics of Digitization (co-author presented), Palo Alto, CA
January 2011, Sixth bi-annual Conference on The Economics of Intellectual Property,
Software and the Internet (2 papers, plenary speaker), Toulouse, France
January 2011, MSI Young Scholars Conference, Park City, UT
December 2010, Workshop on Information Systems and Economics, Washington
University of St. Louis (co-author presented), St. Louis, MO
December 2010, OECD Economics of Privacy Roundtable, Paris, France
November 2010, Net Institute Conference, New York, NY
October 2010, Workshop on Media Economics and Public Policy (co-author presented),
New York, NY
October 2010, Workshop on Health IT and Economics, Washington, DC
September 2010, ITIF and CAGW Privacy Working Group Meetings, Washington, DC
September 2010, Medical Malpractice Conference, Mohegan, CT
September 2010, Search and Web Advertising Strategies and Their Impacts on Consumer
Workshop, Paris, France
July 2010, NBER Meetings (IT), Cambridge, MA
July 2010, NBER Meetings (Healthcare and IT), Cambridge, MA
July 2010, SICS, Berkeley, CA
July 2010, Keynote Speaker, 8th ZEW Conference on the Economics of Information and
Communication Technologies, Mannheim, Germany
June 2010, American Society of Health Economists Conference, Cornell, NY
June 2010, Marketing Science (2 papers), Koeln, Germany
June 2010, Workshop on the Economics of Information Security (2 papers), Harvard, MA

App. 2069

69.
70.
71.
72.
73.
74.
75.
76.
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78.
79.
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81.
82.
83.
84.
85.
86.
87.
88.
89.
90.
91.
92.
93.
94.
95.
96.
97.
98.
99.
100.

January 2010, AEA Meetings, Atlanta, GA
December 2009, Workshop on Information Systems and Economics, Scottsdale, AZ
November 2009, WPP/Google Marketing Awards, Cambridge, MA
July 2009, NBER meetings (IT), Cambridge, MA
June 2009, IHIF Debate on Privacy, Washington, DC
June 2009, Marketing Science, Ann Arbor, MI
April 2009, International Industrial Organization Conference, Boston, MA
January 2009, Information Security Best Practices Conference, Philadelphia, PA
January 2009, Modeling Social Network Data Conference, Philadelphia, PA
July 2008, NBER Meetings (Productivity), Cambridge, MA
July 2008, SICS, Berkeley, CA
July 2008, Fourth Workshop on Ad Auctions, Chicago, MA
June 2008, Marketing Science, Vancouver, BC
May 2008, International Industrial Organization Conference, Richmond, VA
April 2008, Net Institute Conference, New York, NY
November 2007, NBER Health Meetings (Co-author presented), Boston, MA
July 2007, SICS, Berkeley, CA
June 2007, Workshop on the Economics of Information Security, Pittsburgh
June 2007, Choice Symposium, Philadelphia, PA
May 2007, eCommerce Research Symposium, Stamford, CT
April 2007, Net Institute Conference, New York, NY
April 2007, International Industrial Organization Conference, Savannah, GA
March 2007, Health Economics Conference, Tucson, AZ
February 2007, NBER Winter Meetings, Palo Alto, CA
January 2007, Economics of the Software and Internet Industries (2 Papers), Toulouse,
France
October 2006, QME Conference, Stanford University, CA
June 2006, Marketing Science, Pittsburgh, PA
April 2006, International Industrial Organization Conference, Boston, MA
October 2005, NEMC Conference, Boston, MA
October 2005, TPRC Conference, Washington, DC
June 2005, CRES Industrial Organization Conference, Washington University in St.
Louis, MO
July 2002, Payment Systems Conference, IDEI, Toulouse, France

App. 2070

G RANTS
2013
2012
2011
2011
2011
2011
2011
2011
2010
2010
2009
2009
2009
2008
2007
2006

MSI research grant 4-1840
Google Australia
Tilburg Law and Economics Center (TILEC) IIPC grant
Google Grant
Junior Faculty Research Assistance Program
Net Institute Grant
NBER Digitization Grant
NSF CAREER Award
Time-Warner Research Program on Digital Communications
Net Institute Grant
Net Institute Grant
The James H. Ferry, Jr. Fund for Innovation in Research
Education
Google/WPP Grant
Net Institute Grant
Net Institute Grant
Net Institute Grant

$10,200
$50,000
$21,000
$50,000
$30,000
$6,000
$20,000
$502,000
$20,000
$6,000
$6,000
$50,000
$55,000
$15,000
$8,000
$8,000

P ROFESSIONAL S ERVICE
• Associate Editor: Management Science, International Journal of Research in Marketing
• Associate Editor: Information Systems Research, Special Issue on Social Media and
Business Transformation
• Departmental Editor: Quantitative Marketing and Economics
• Editor: Journal of Network Economics
• Editor: The Economics of the Internet, Palgrave Dictionary of Economics
• Co-Editor: NBER: The Economics of Digitization - An Agenda
• Co-Editor: Information Economics and Policy, Special Issue on Economics of Digital
Media Markets
• Editorial Review Board: Journal of Marketing, Journal of Marketing Research,
Marketing Science, ISR Special Issue on Managing Digital Vulnerabilities
• Advisory Board: Future of Privacy Forum
• Conference Program Committees
– 2015 Scientific Committee: Competition, Standardization and Innovation
– 2015 Scientific Committee: Intellectual Property Statistics for Decision Makers
– 2015 Associate Editor: ICIS 2015, Healthcare track
– 2015 Scientific Committee: European Association for Research in Industrial Economics
– 2015 Program Committee: ACM Conference on Economics and Computation
– 2015 Program Committee: Workshop on the Economics of Information Security
– 2015 Chief-Organizer: Quantitative Marketing and Economics Conference

App. 2071

– 2015 Scientific Committee: ZEW Conference on the Economics of Information and
Communication Technologies
– 2014 Scientific Committee: European Association for Research in Industrial Economics
– 2014 Scientific Committee: Conference on the Economics of Information and
Communication Technologies
– 2014 Program Committee: International Conference on Big Data and Analytics in
Healthcare
– 2013 Program Committee: Quantitative Marketing and Economics
– 2013 Scientific Committee: European Association for Research in Industrial Economics
Conference
– 2013 Scientific Committee: Conference on the Economics of Information and
Communication Technologies
– 2013 Program Committee: Workshop on the Economics of Information Security
– 2013 Associate Editor of Personal Data Markets Track: ECIS 2013
– 2012 Program Committee: European Association for Research in Industrial Economics
Conference
– 2012 Program Committee (Conference Organizer) NBER: The Economics of Digitization
Pre-Conference, June 2012
– 2012 Scientific Committee: Conference on the Economics of Information and
Communication Technologies
– 2012 Senior Program Committee: 13th ACM Conference on Electronic Commerce
– 2012 Program Committee: Workshop on the Economics of Information Security
– 2011 Scientific Committee: European Association for Research in Industrial Economics
Conference
– 2011 Scientific Committee: Conference on the Economics of Information and
Communication Technologies
– 2011 Program Committee: Ad Auctions Workshop
– 2011 Program Committee: Workshop on the Economics of Information Security
– 2010 Program Committee: Workshop on IT and Economic Growth
– 2010 Program Committee: Conference on Health IT and Economics
– 2010 Program Committee: Workshop on the Economics of Information Security
– 2009 Program Committee: Workshop on the Economics of Information Security
– 2008 Program Committee: Workshop on the Economics of Information Security
– 2008 Program Committee: Ad Auctions Workshop

MIT S ERVICE
-

2015 EMBA Committee
2014 MIT Sloan Gender Equity Committee
2013-2014 Group Head, Marketing Group
2013-2014 Chair, Marketing Faculty Search Committee
2013-2014 MIT Committee on Undergraduate Admissions and Financial Aid
2011 North East Marketing Conference Coordinator

App. 2072

- 2011 MIT Sloan Marketing Conference, Panel Moderator
- 2011 Sloan Women in Management Conference, Panel Moderator
- 2005, 2008, 2012 Marketing Faculty Search Committee

A DVISING
•
•
•
•
•

2014:
2012:
2010:
2008:
2007:

Abhishek Nagaraj, PhD Thesis advisor
Cristina Nistor, PhD Thesis advisor
Katherine Molina, Masters Thesis
Dinesh Shenoy, Masters Thesis
James Kelm, Masters Thesis

E XPERT A DVICE
• Cleary Gottlieb Steen & Hamilton LLP: Deposed and testified as Expert Witness in
Bankruptcy Proceedings
• Gibson Dunn: Deposed as Expert Witness in Civil Litigation Proceedings.

T EACHING
-

15.818, Pricing (MBA Elective) 200615.732, Marketing Management for Senior Executives 201215.s07, Pricing (EMBA Elective) 201215.838, Doctoral Seminar, Spring 2006, Fall 2007, Fall 2013
Guest Lecturer: HST.936: Health information systems to improve quality of care in
resource-poor settings, 2014
Executive Education: Strategic Marketing for the Technical Executive, 2012Executive Education: Systematic Innovation of Products, Processes, and Services, 2013Executive Education: Platform Strategy: Building and Thriving in a Vibrant Ecosystem,
2014Executive Education: Global Executive Academy (multi-language), 2013Executive Education: Entrepreneurship Development Program, 2012-

App. 2073

EXHIBIT EEE

App. 2074

Exhibit EEE: List of Testimony
Catherine Tucker
GO Computer, Inc. et al. v. Microsoft Corporation, Superior Court of the State Of California for
the City and County of San Francisco, Case No. CGC-05-442684
Deposition Testimony (2015)
Queen’s University at Kingston and PARTEQ Research and Development Innovations, v.
Samsung Electronics Co., Ltd., et al., Civil Action No. 2:14-cv-53-JRG-RSP
Deposition Testimony (2015)
In re: Chapter 11, Nortel Networks, Inc., et al., Debtors, U.S. Bankruptcy Court, District of
Delaware, Case No. 09-10138(KG) (Jointly Administered), Re Dkt No. 13208
Deposition and Trial Testimony (2014)
Angel Fraley, et al., Plaintiffs, v. Facebook, Inc., a corporation; and DOES 1-100, Defendants,
U.S. District Court, Northern District of California, Case No. 5:11-cv-01726-LHK
Deposition Testimony (2012)

EEE-1

App. 2075

EXHIBIT FFF

App. 2076

Exhibit FFF: Materials Considered
I.

Court Documents and Expert Reports

Consolidated Amended Class Action Complaint, Matthew Campbell, Michael Hurley, and David
Shadpour et al. v. Facebook, Inc., Case No. 4:13-cv-05996, United States District Court,
Northern District of California, April 25, 2014.
Defendant Facebook’s Inc.’s Second Supplemental Responses and Objections to Plaintiffs
Narrowed Second Set of Interrogatories, Matthew Campbell, Michael Hurley, and David
Shadpour v. Facebook, Inc., Case No. C 13-05996 PJH (MEJ), United States District Court,
Northern District of California, October 29, 2015.
Defendant Facebook’s Inc.’s Supplemental Responses and Objections to Plaintiffs First Set of
Interrogatories, Matthew Campbell, Michael Hurley, and David Shadpour v. Facebook, Inc.,
Case No. C 13-05996 PJH, United States District Court, Northern District of California,
September 8, 2015.
Defendant Facebook, Inc.’s Supplemental Responses and Objections to Plaintiffs’ Request for
Production Nos. 54, 55, and 57, Matthew Campbell, Michael Hurley, and David Shadpour v.
Facebook, Inc., Case No. C 13-05996 PJH (MEJ), United States District Court, Northern District
of California, October 28, 2015.
January 15, 2016, Declaration of Alex Himel.
January 15, 2016, Declaration of Chris Chorba.
January 15, 2016, Declaration of Dan Fechete.
January 15, 2016, Declaration of Michael Adkins.
Plaintiffs’ Motion for Class Certification, Matthew Campbell and Michael Hurley et al. v.
Facebook, Inc., Case No. C 13-05996 PJH, United States District Court, Northern District of
California, November 13, 2015.
Plaintiffs’ Supplemental Initial Disclosures Pursuant to Fed. R. Civ. P. 26(a)(1), Matthew
Campbell, Michael Hurley, and David Shadpour et al. v. Facebook, Inc., Case No. 4:13-cv05996-PJH, United States District Court, Northern District of California, April 27, 2015.
Report of Fernando Torres In Support of Plaintiffs’ Motion for Class Certification, Matthew
Campbell and Michael Hurley et al. v. Facebook, Inc., Case No. C 13-05996 PJH (MEJ), United
States District Court, Northern District of California, November 13, 2015.

FFF-1

App. 2077

Report of Jennifer Golbeck In Support of Plaintiffs’ Motion for Class Certification, Matthew
Campbell and Michael Hurley et al. v. Facebook, Inc., Case No. C 13-05996 PJH (MEJ), United
States District Court, Northern District of California, November 13, 2015.
Updated Report of Fernando Torres In Support of Plaintiffs’ Motion for Class Certification,
Matthew Campbell and Michael Hurley et al. v. Facebook, Inc., Case No. C 13-05996 PJH
(MEJ), United States District Court, Northern District of California, January 13, 2016.
II.

Bates Stamped Documents

FB000008505
FB000006178
FB000007286
FB000000298
FB000000166
FB000000163
FB000011715
FB000026793
FB000001454
CAMPBELL000004-5
CAMPBELL000007
CAMPBELL000010
CAMPBELL000014
CAMPBELL000021
CAMPBELL000029
CAMPBELL000038
CAMPBELL000052
CAMPBELL000075-77
CAMPBELL000089

FFF-2

App. 2078

CAMPBELL000110
CAMPBELL000160
CAMPBELL000168-70
CAMPBELL000184
CAMPBELL000452
HURLEY000001-3
III.

Depositions

John Orsi, III, August 10, 2015.
Michael D. Campbell, May 19, 2015.
Jennifer Golbeck, December 16, 2015.
Elisabeth Hartner, August 7, 2015.
Michael Hurley, July 9, 2015.
David Shadpour, October 1, 2015.
Fernando Torres, December 18, 2015.
Jeffrey Woodmansee, August 11, 2015.
IV.

Publicly Available

“Advertising Standards,” IRS, last updated 07-Jan-2016, https://www.irs.gov/uac/AdvertisingStandards, viewed January 15, 2016.
Brantley, Max, “Mark Darr’s Ethical Compass Fails Again,” August 30, 2013,
http://www.arktimes.com/ArkansasBlog/archives/2013/08/30/mark-darrs-ethical-compass-failsagain, viewed January 8, 2016.
Brock, Roby, “Bill Clinton to Give Little Rock Speech On Health Care Law,” Talk Business &
Politics, August 28, 2013, http://talkbusiness.net/2013/08/bill-clinton-to-give-little-rock-speechon-health-care-law/, viewed January 8, 2016.
Chieruzzi, Massimo, “Buying Facebook Likes Sucks, Here’s The Data To Prove It!,”
AdEspresso, November 19, 2014, https://adespresso.com/academy/blog/buy-facebook-likes/,
viewed December 12, 2015.

FFF-3

App. 2079

Choudary, Sangeet Paul, “The Rise of Social Graphs for Businesses,” Harvard Business Review,
February 2, 2015, https://hbr.org/2015/02/the-rise-of-social-graphs-for-businesses, viewed
January 6, 2016.
Clark, Lauren, “Bills Focused on Arkansas Oil and Gas Industry Review at Capitol,” THV11,
September 14, 2011, http://archive.thv11.com/news/article/149963/2/On-Assignment-Oil-andgas-bills-on-the-table-at-Capitol#, viewed January 6, 2016.
Delo, Cotton, “Facebook Launches New Retargeting Alternative to FBX: Targeting to Use
Tracking Software That Marketers Can Attach to Websites and Mobile Apps,” AdvertisingAge,
October 15, 2013, http://adage.com/article/digital/facebook-launches-retargeting-alternativefbx/244746/, viewed January 3, 2016.
Delo, Cotton, and Michael McCarthy, “GM Returns to Facebook Advertising after Public Split a
Year Ago,” AdvertisingAge, April 9, 2013, http://adage.com/article/digital/gm-returns-facebookadvertising-public-split/240785/, viewed January 3, 2016.
Edelman, David, and Brian Salsberg, “Beyond Paid Media: Marketing’s New Vocabulary,”
McKinsey&Company, November 2010,
http://www.mckinsey.com/insights/marketing_sales/beyond_paid_media_marketings_new_voca
bulary, viewed January 11, 2016.
Fisher, Max, “Why Australia Hates Halloween,” Vox, October 31, 2014,
http://www.vox.com/2014/10/31/7137369/why-australia-hates-halloween, viewed January 6,
2016.
Golbeck, Jennifer, “The Curly Fry Conundrum: Why Social Media ‘Likes’ Say More than You
Might Think,” TEDxMidAtlantic 2013,
https://www.ted.com/talks/jennifer_golbeck_the_curly_fry_conundrum_why_social_media_likes
_say_more_than_you_might_think, viewed December 11, 2015.
Greg Leding: Join Our Mailing List,
https://web.archive.org/web/20110715232729/http://gregleding.com/, viewed January 15, 2016.
He, Ray C., “Introducing New Like and Share Buttons,” Facebook for Developers, November 6,
2013, https://developers.facebook.com/blog/post/2013/11/06/introducing-new-like-and-sharebuttons/, viewed December 11, 2015.
Home: Old South Church, http://oldsouth.org, viewed January 11, 2016.
Jack, Tyler, “Let’s Help Chad Watkins with Legal Fees!” FundRazr,
https://fundrazr.com/campaigns/9fHh3/ab/92V1Z1, viewed January 8, 2016.

FFF-4

App. 2080

Lafferty, Justin, “Yahoo Ends Social Bar, Cutting off Facebook Integration,” October 4, 2013,
http://www.adweek.com/socialtimes/yahoo-social-bar-cutting-off-facebook-integration/296317,
viewed January 8, 2016.
Lewis, Randall A., and Justin M. Rao, “The Unfavorable Economics of Measuring the Returns to
Advertising,” The Quarterly Journal of Economics, first published online July 6, 2015
doi:10.1093/qje/qjv023.
McClain, Dylan Loeb, “Good at Chess? A Hedge Fund May Want to Hire You,” The New York
Times, September 29, 2011,
https://web.archive.org/web/20110930171014/http://dealbook.nytimes.com/2011/09/29/good-atchess-a-hedge-fund-may-want-to-hire-you/, viewed December 11, 2015.
Reynolds, Helen, “How to Respond When Rumours Start to Spread on Facebook,” The
Guardian, May 14, 2014, http://www.theguardian.com/local-governmentnetwork/2014/may/14/how-to-respond-when-rumours-spread-on-facebook, viewed December
11, 2015.
Steinhauer, Jennifer, and Carl Hulse, “Vote on Boehner Plan Delayed Amid Opposition,” The
New York Times, July 26, 2011,
https://web.archive.org/web/20110825191937/http://www.nytimes.com/2011/07/27/us/politics/2
7fiscal.html, viewed January 6, 2016.
Trust, Gary, “Ask Billboard: How Does the Hot 100 Work?” Billboard, September 29, 2013,
http://www.billboard.com/articles/columns/ask-billboard/5740625/ask-billboard-how-does-thehot-100-work, viewed December 11, 2015.
Weigley, Samuel, “10 Web Sites Where Surfers Spend the Most Time,” USA Today, March 9,
2013, http://www.usatoday.com/story/money/business/2013/03/09/10-web-sites-mostvisited/1970835/, viewed January 8, 2016.
“2015 Fab 15 Toys,” Kmart, http://www.kmart.com/en_us/dap/fab-15-toys.html, viewed January
3, 2016.
“Amniotic Band Syndrome,” Fetal Health Foundation,
https://web.archive.org/web/20150910004526/http://www.fetalhealthfoundation.org/amnioticband-syndrome/, viewed January 6, 2016.
“Behind Kmart’s Fab 15 List: How We Identify Hot Toy Trends,” SEARS HOLDINGS: SHC
Speaks, September 24, 2014, http://blog.searsholdings.com/inside-shc/behind-kmarts-fab-15-listhow-we-identify-hot-toy-trends/, viewed on January 15, 2016.

FFF-5

App. 2081

“The Best WordPress Facebook Widgets,” Elegant Themes Blog, January 15, 2015,
https://www.elegantthemes.com/blog/resources/the-best-wordpress-facebook-widgets, viewed
December 12, 2015.
“Buy Real Facebook Likes,” Buylikesandfollowers.net,
http://www.buylikesandfollowers.net/buy-facebook-likes-cheap.html, viewed December 12,
2015.
Events Insider, http://bostoneventsinsider.com/subscribe.html/, viewed December 17, 2015.
“Facebook Reports Second Quarter 2015 Results,” Facebook Investor Relations, July 29, 2015,
http://investor.fb.com/releasedetail.cfm?ReleaseID=924562, viewed January 3, 2016.
“Facebook Advertising Targeting Options,” Facebook for Business,
https://www.facebook.com/business/products/ads/ad-targeting/, viewed January 6, 2016.
“How Many Sites Have Facebook Integration? You’d Be Surprised,” Pingdom.com, June 18,
2012, http://royal.pingdom.com/2012/06/18/how-many-sites-have-facebook-integration-youdbe-surprised/, viewed December 11, 2015.
INTA Enforcement Committee: Discovery Practices & Procedures Subcommittee, “Wayback
Machine Memo,” November 2, 2009,
http://www.inta.org/Advocacy/Documents/INTAWaybackMachine2009.pdf.
“Joshua Mahar Photography,” Zenfolio, http://jmahar.zenfolio.com/we2011, viewed January 3,
2016.
“Like Button for the Web,” Facebook for Developers,
https://developers.facebook.com/docs/plugins/like-button, viewed December 12, 2015.
“More Matching Capabilities with Custom Audiences,” Facebook Marketing Partners,
November 30, 2015, https://facebookmarketingpartners.com/partner-news/more-matchingcapabilities-with-custom-audiences/, viewed January 3, 2016.
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-you-1000-facebook-likes, viewed December 12, 2015.
“New York Times Most Popular Articles,” The New York Times, http://www.nytimes.com/mostpopular, viewed December 11, 2015.
“NPR Bestseller List: Week of Oct. 1, 2015,” NPR,
http://www.npr.org/books/bestsellers/2015/week40/, viewed December 11, 2015.

FFF-6

App. 2082

“Privacy Policy,” The New York Times,
http://www.nytimes.com/content/help/rights/privacy/policy/privacy-policy.html, viewed January
11, 2016.
“The Real Reason the US Post Office is Going Bankrupt,” Realista, February 11, 2013,
http://www.realitista.com/post/42860370390/the-real-reason-the-us-post-office-is-going, viewed
January 3, 2016.
Restore the Fourth, http://restorethe4th.com/, viewed January 8, 2016.
Restore the Fourth,
https://web.archive.org/web/20151015204749/http://www.restorethefourth.net/, viewed January
6, 2016.
“Social Plugins,” Facebook for Developers, https://developers.facebook.com/docs/plugins,
viewed December 12, 2015.
“Targeting,” Facebook for Business, https://www.facebook.com/business/products/ads/adtargeting/, viewed January 6, 2016.
“The Thin Line between Liking a Brand and Liking Its Social Marketing,” eMarketer,
September 8, 2010, http://www.emarketer.com/Article.aspx?R=1007912, viewed January 8,
2016.
“Top Sites in United States,” Alexa, http://www.alexa.com/topsites/countries/US, viewed on
January 15, 2016.
Facebook post by Cassowary Coast Regional Council, December 1, 2013,
https://www.facebook.com/cassowarycoastregionalcouncil/posts/588646594521749, viewed
December 11, 2015.
Facebook Q2 2015 Results, Facebook Investor Relations, available at
http://investor.fb.com/results.cfm.
Facebook 10-Ks, 2012-2014.
Facebook 10-Qs, 2013-2015.
Amazon.com, viewed January 11, 2016.
Baidu.com, viewed January 11, 2016.
Google.com, viewed January 11, 2016.
Qq.com, viewed January 11, 2016.

FFF-7

App. 2083

Taobao.com, viewed January 11, 2016.
Twitter.com, viewed January 11, 2016.
Wikipedia.org, viewed January 11, 2016.
Yahoo.com, viewed January 11, 2016.
Youtube.com, viewed January 11, 2016.
V.

Articles and Books

Banerjee, Abhijit V., “A Simple Model of Herd Behavior,” Quarterly Journal of Economics,
Vol. 107, No. 3, August 1992, pp. 797-817.
“New York Times Most Popular Articles,” The New York Times, http://www.nytimes.com/mostpopular, viewed December 11, 2015.
Bikhchandani, Sushil, David Hirshleifer, and Ivo Welch, “A Theory of Fads, Fashion, Custom,
and Cultural Change as Informational Cascades,” Journal of Political Economy, Vol. 100, No. 5,
1992, pp. 992-1026.
Damodaran, Aswath, “Research and Development Expenses: Implications for Profitability
Measurement and Valuation,” NYU Stern School of Business,
http://people.stern.nyu.edu/adamodar/pdfiles/papers/R&D.pdf.
Friggeri, Adrien, et al, “Rumor Cascades,” Proceedings of the Eighth International AAAI
Conference on Weblogs and Social Media, 2014.
Ghose, Anindya, Panagiotis G. Ipeirotis, and Beibei Li, “Designing Ranking Systems for Hotels
on Travel Search Engines by Mining User-Generated and Crowdsourced Content, Marketing
Science, Vol. 31, No .3, May–June 2012, pp. 493-520.
Goldfarb, Avi, and Catherine Tucker, “Advertising Bans and the Substitutability of Online and
Offline Advertising,” Journal of Marketing Research, Vol. 48, No .2, 2011, pp. 207-227.
Greenstein, Shane M., Avi Goldfarb, and Catherine E. Tucker, editors, The Economics of
Digitization, Edward Elgar Publishing, 2013.
John, Leslie et al., “What are Facebook ‘Likes’ Really Worth?,” HBS Working Paper, 2015,
http://rady.ucsd.edu/docs/events/lesliejohn.pdf.
Mochon, Daniel, Karen Johnson, Janet Schwartz, and Dan Ariely, “How much is a like worth? A
field experiment of Facebook pages,” Tulane University Working Paper – Advances in
Consumer Research, vol. 42, 2015

FFF-8

App. 2084

Reilly, R. F. and R.P. Schweihs, Valuing Intangible Assets, McGraw Hill, 1999.
Resnick, Paul, and Hal R. Varian, “Recommender Systems,” Communications of the ACM, Vol.
40, No. 3, March 1997, pp. 1-3.
Smith, G.V. and R.L. Parr, Valuation of Intellectual Property and Intangible Assets, John Wiley
& Sons, 2000.
Torkjazi, Mojtaba, Reza Rejaie, and Walter Willinger, “Hot Today, Gone Tomorrow: On the
Migration of MySpace Users,” Proceedings of the 2nd ACM Workshop on Online Social
Networks, 2009.
Tucker, Catherine, “Social Advertising,” February 15, 2012, SSRN
(http://ssrn.com/abstract=1975897).
Tucker, Catherine E., “Social Networks, Personalized Advertising, and Privacy Controls,”
Journal of Marketing Research, Vol. 51, No. 5, 2014, pp. 546-562.
Tucker, Catherine, and Alexander Marthews, “Social Networks, Advertising, and Antitrust,”
George Mason Law Review, Vol. 19, 2012, pp. 1211-1227.
Tucker, Catherine, and Anja Lambrecht, “Can Big Data Protect a Firm from Competition?”
December 18, 2015, SSRN (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2705530).
Tucker, Catherine, and Juanjuan Zhang, “Growing Two-Sided Networks by Advertising the User
Base: A Field Experiment, Marketing Science, Vol. 29, No .5, 2010, pp. 805-814.
Tucker, Catherine, and Juanjuan Zhang, “How Does Popularity Information Affect Choices? A
Field Experiment,” Management Science, Vol. 57, No. 5, 2011, pp. 828-842.
Tucker, Catherine, Juanjuan Zhang, and Ting Zhu, “Days on Market and Home Sales,” The
RAND Journal of Economics, 2013.
Walsh, Toby, “Search in a Small World,” IJCAI, Vol. 99, 1999.

FFF-9

App. 2085

EXHIBIT GGG

App. 2086

($ mil)
525
590
637
780
679
848
962
1,206
1,179
1,308
1,514
1,864
1,739
1,967
2,256

Canada ($ mil)
419
479
538
631
552
721
832
1,068
1,039
1,175
1,362
1,709
1,592
1,826
2,120

Revenue, US and

US and Canada
Cost of Revenue
26%
31%
26%
25%
28%
26%
25%
19%
18%
16%
18%
17%
18%
17%
16%
Sales
14%
33%
13%
12%
14%
15%
12%
11%
13%
12%
12%
16%
17%
15%
16%

Marketing &
Administrative
10%
39%
12%
11%
12%
10%
8%
10%
7%
7%
8%
9%
8%
8%
8%

General &

App. 2087

Development
14%
60%
19%
19%
20%
19%
18%
16%
18%
17%
19%
29%
30%
29%
28%

Research &

Expenses as a Percentage of Revenue

Sources:
1. Facebook, Inc. Form 10-K 2013-2014. Retrieved from SEC EDGAR website http://www.sec.gov/edgar.shtml.
2. Facebook, Inc. Form 10-Q 2013-2015. Retrieved from SEC EDGAR website http://www.sec.gov/edgar.shtml.

Quarter
Q1'12
Q2'12
Q3'12
Q4'12
Q1'13
Q2'13
Q3'13
Q4'13
Q1'14
Q2'14
Q3'14
Q4'14
Q1'15
Q2'15
Q3'15

Advertising

Total Revenue,

Revenue

Exhibit GGG
Facebook Quarterly Revenue and Expenses
Q1'12-Q3'15

Excluding R&D
50%
103%
51%
48%
54%
50%
45%
40%
39%
35%
37%
42%
44%
40%
39%

Total Expenses,
Total Expenses
64%
163%
70%
67%
74%
69%
63%
56%
57%
52%
56%
71%
74%
69%
68%

EXHIBIT HHH

App. 2088

Average Expenses as a
Percentage of Revenue 3
[D]

Annual Profit ($ mil)
2,010
2,010
2,010
2,010
2,010
2,010
2,010
2,010
Total Value:

Discount Factor 4
0.8427
0.7102
0.5985
0.5044
0.4251
0.3582
0.3019
0.2544

2,010

Annual Profit ($ mil)
[E] = (4*[C]) * (1-[D])

App. 2089

Sources:
1. Facebook, Inc. Form 10-K 2013-2014. Retrieved from SEC EDGAR website http://www.sec.gov/edgar.shtml.
2. Facebook, Inc. Form 10-Q 2013-2015. Retrieved from SEC EDGAR website http://www.sec.gov/edgar.shtml.
3. Report of Fernando Torres In Support of Plaintiffs’ Motion for Class Certification, Matthew Campbell and Michael Hurley et al. v. Facebook, Inc. , Case No. C 1305996 PJH (MEJ), United States District Court, Northern District of California, November 13, 2015.

4. Calculated as 1/(1+r) n, where r is the 18.66% discount rate calculated by Dr. Torres and n is the number of years. See Torres Report, ¶42 and Exhibit 1.
5. Annual profit ($ mil) * discount factor.

8,031

Discounted Value ($ mil)5
1,694
1,427
1,203
1,014
854
720
607
511

963

89.96%

Social Graph Valuation

48%

US Ad. Revenue per
Quarter ($ mil)
[C] = [A] * [B]

Ratio of US to US and
Canada Population 2
[B]

Annual Profit Calculation

Notes:
1. Average of Q1'12-Q3'15 advertising revenue, US and Canada as presented in Exhibit 1.
2. See Torres Report, footnote 66.
3. Average of Q1'12-Q3'15 total expenses as a % of revenue, excluding R&D as presented in Exhibit 1.

Year
1
2
3
4
5
6
7
8

Facebook Average Quarterly
Advertising Revenue, US and
Canada ($ mil)1
[A]
1,071

Exhibit HHH
Torres Social Graph Valuation
Q1'12-Q3'15 Facebook Revenues and Expenses

EXHIBIT III

App. 2090

Average Expenses as a
Percentage of Revenue 3
[D]

Total Value:

Discount Factor 4
0.8427
0.7102
0.5985
0.5044
0.4251
0.3582
0.3019
0.2544

Social Graph Valuation

1,910

Annual Profit ($ mil)
[E] = (4*[C]) * (1-[D])

App. 2091

Sources:
1. Facebook, Inc. Form 10-K 2014. Retrieved from SEC EDGAR website http://www.sec.gov/edgar.shtml.
2. Facebook, Inc. Form 10-Q 2014-2015. Retrieved from SEC EDGAR website http://www.sec.gov/edgar.shtml.
3. Report of Fernando Torres In Support of Plaintiffs’ Motion for Class Certification, Matthew Campbell and Michael Hurley et al. v. Facebook, Inc. , Case No. C 1305996 PJH (MEJ), United States District Court, Northern District of California, November 13, 2015.

4. Calculated as 1/(1+r) n, where r is the 18.66% discount rate calculated by Dr. Torres and n is the number of years. See Torres Report, ¶42 and Exhibit 1.
5. Annual profit ($ mil) * discount factor.

7,631

Discounted Value ($ mil)5
1,609
1,356
1,143
963
812
684
577
486

1,459

89.96%

Annual Profit ($ mil)
1,910
1,910
1,910
1,910
1,910
1,910
1,910
1,910

67%

US Ad. Revenue per
Quarter ($ mil)
[C] = [A] * [B]

Ratio of US to US and
Canada Population 2
[B]

Annual Profit Calculation

Notes:
1. Average of Q3'14-Q2'15 advertising revenue, US and Canada as presented in Exhibit 1.
2. See Torres Report, footnote 66.
3. Average of Q3'14-Q2'15 total expenses as a % of revenue as presented in Exhibit 1.

Year
1
2
3
4
5
6
7
8

Facebook Average Quarterly
Advertising Revenue, US and
Canada ($ mil)1
[A]
1,622

Exhibit III
Torres Social Graph Valuation
Q3'14-Q2'15 Facebook Revenues and Expenses
Including Research & Development Expenses

EXHIBIT JJJ

App. 2092

Average Expenses as a
Percentage of Revenue 3
[D]

Total Value:

Discount Factor 4
0.8427
0.7102
0.5985
0.5044
0.4251
0.3582
0.3019
0.2544

Social Graph Valuation

1,097

Annual Profit ($ mil)
[E] = (4*[C]) * (1-[D])

App. 2093

Sources:
1. Facebook, Inc. Form 10-K 2013-2014. Retrieved from SEC EDGAR website http://www.sec.gov/edgar.shtml.
2. Facebook, Inc. Form 10-Q 2013-2015. Retrieved from SEC EDGAR website http://www.sec.gov/edgar.shtml.
3. Report of Fernando Torres In Support of Plaintiffs’ Motion for Class Certification, Matthew Campbell and Michael Hurley et al. v. Facebook, Inc. , Case No. C 1305996 PJH (MEJ), United States District Court, Northern District of California, November 13, 2015.

4. Calculated as 1/(1+r) n, where r is the 18.66% discount rate calculated by Dr. Torres and n is the number of years. See Torres Report, ¶42 and Exhibit 1.
5. Annual profit ($ mil) * discount factor.

4,383

Discounted Value ($ mil)5
925
779
657
553
466
393
331
279

963

89.96%

Annual Profit ($ mil)
1,097
1,097
1,097
1,097
1,097
1,097
1,097
1,097

72%

US Ad. Revenue per
Quarter ($ mil)
[C] = [A] * [B]

Ratio of US to US and
Canada Population 2
[B]

Annual Profit Calculation

Notes:
1. Average of Q1'12-Q3'15 advertising revenue, US and Canada as presented in Exhibit 1.
2. See Torres Report, footnote 66.
3. Average of Q1'12-Q3'15 total expenses as a % of revenue as presented in Exhibit 1.

Year
1
2
3
4
5
6
7
8

Facebook Average Quarterly
Advertising Revenue, US and
Canada ($ mil)1
[A]
1,071

Exhibit JJJ
Torres Social Graph Valuation
Q1'12-Q3'15 Facebook Revenues and Expenses
Including Research & Development Expenses