Deceptive Claims using Fake News Marketing: The

Impact on Consumers

Anita Rao

Booth School of Business

University of Chicago

February 12, 2018

THIS IS A DRAFT. COMMENTS ARE WELCOME, BUT PLEASE DO NOT CITE

WITHOUT AUTHOR’S PERMISSION

Abstract

Fake news can be harmful if it misleads consumers to take actions they otherwise

would not have taken (e.g. vote for another party, purchase an inferior product). How-

ever, if fake news merely confirms existing beliefs without changing consumers’ actions,

the extent of such harm is less severe. The main challenge in identifying the impact

of fake news, is that we do not observe actions before and after the exposure to fake

news. This paper exploits a unique setting where the FTC enabled the shutdown of

ten companies that were operating fake news websites that in reality were advertise-

ments for various products. Using detailed browsing data of these product websites, I

identify the extent of consumer interest in the presence and absence of fake news. The

findings indicate that interest wanes after the shutdown of fake news, but there is some

substitution to other channels such as regular advertisements.

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1 Introduction

Fake news sites have recently garnered a lot of attention because of their potential impact

on the 2016 elections. Sites such as Facebook and Google are under fire for their role as

distributors of fake news. However, the empirical impact of fake news on actual outcomes

is unknown. Alcott and Gentzkow (2017), in the context of the 2016 elections, point out

that it is possible fake news merely strengthened voters’ predetermined beliefs, but did not

change their voting behavior. For example, those likely to vote for Trump are the ones who

believe pro-Trump fake news. It is empirically challenging to measure the true impact of the

fake news, because we do not observe voting behavior prior to the exposure to fake news.

In this paper, using a set of discontinued fake news sites, I disentangle the impact of fake

news from that of regular advertising on consumers’ purchase propensity. In April 2011, the

FTC brought to halt the operations of ten fake news website operating companies. These

ten companies operated over 150 fake news websites with names such as onlinenews6.com

and consumerdigestweekly.com. The product domains that these fake news sites referred

consumers to, typically sold purported weight-loss products and colon cleansing products.

These companies used both direct advertising through sites such as google, and fake news

“ads” to propagate their products. The fake news sites falsely reported, in a journalistic

manner, the positive impact of using the products. The FTCs actions were directed toward

the fake news operating companies, and not the advertising companies, which were under no

restraint and could continue to operate. The difference in product domain visitations before

and after the FTC complaint identify the causal impact of the visitations due to the fake

news.

Using detailed browsing data from comScore, which also tracks referral domains, one

can identify whether consumers visited the product’s website directly, were referred from a

general search site such as google, or were referred from a fake news site. We also observe the

chronological sequence of website visitations, i.e., did a user first reach the product domain

via an advertisement and subsequently visited the domain directly.

The findings indicate visits to product domains that used fake news as an advertising

means drop after the shutdown of the fake news operating sites. Breaking down the visits

into organic visits or visits through advertisement referrals suggests the drop in visits comes

from a drop in referrals via fake news (mechanistically) as well as a drop in direct visits.

However, there appears to be an increase in visitations via regular advertisements, suggesting

that fake news and advertisements are substitutes atleast in these product domains.

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Contribution The impact of fake news has largely been studied in the political domain

(e.g., Guess, Nyhan and Reifler 2018; Alcott and Gentzkow 2017), with little work in the

domain of consumer products. That consumers might choose to consume news geared to-

wards their preferences has been studied in Gentzkow and Shapiro (2006, 2010), Zhu and

Dukes (2015) and Simonov (2018) to name a few. This paper contributes to the literature on

media slant by focusing on fake news as a means of advertising, and its impact on consumer

purchases as opposed to media consumption in a political setting.

This paper also contributes to the literature on deceptive practices undertaken by firms

to win consumers’ walletshare. Rao and Wang (2017) study the impact of false health claims

on consumer demand, Zinman and Zitzewitz (2013) study ski resorts’ deceptive reporting

of snowfall to attract demand, Luca and Zervas (2016) study review fraud by restaurants,

and Chiou and Tucker (2016) study ads that present selective information. However, little

empirical work documents the impact of deceptive advertising in the form of fake news.

This paper studies advertisements geared to appear like news, bringing together the

literature that studies news consumption and literature that studies firms’ deceptive practices

and their impact on consumers purchase decisions. Unlike the previous literature this paper

studies not only what is claimed but also how it is depicted, i.e., false claims made to appear

like articles by legit news organizations. A closely related paper is Chiou and Tucker (2018)

who show that after Facebook banned fake news ads from their ad networks, sharing of

fake news articles related to anti-vaccines dropped dramatically. In this paper, I further

study what happens to consumer behavior after the ban on fake news style marketing: do

consumers nonetheless find the product domain in the absence of the fake news ad either

directly or through regular online ads?

2 Data and Descriptives

In April 2011, the FTC identified ten companies as operating fake news websites1. Table 1

lists a subset of the fake news websites operated by the ten companies as recovered from the

FTC complaint files accessed using the Bloomberg Law database. Figure 3 in the Appendix

shows an article from one of these websites, extracted from one of the FTC court dockets

(FTC v Beony International, Attachment C), which highlights the nature of the fake news.

In short, in these fake articles, the “journalists” voice their skepticism of the products, claim

to try it for themselves, and report their “fake” findings of weight loss. Figure 5 shows

another such article from another fake news operating website as cited in one of the FTC

1https://www.ftc.gov/news-events/press-releases/2011/04/ftc-seeks-halt-10-operators-fake-news-sites-making-deceptive

3

case documents (Coulomb Media Inc, FTC File Number: 112 3072).

Table 1: Fake news domains and their visit count

Company Fake News Domain names 2010 2011 2012

Ambervine usahealthnews.org 8 0 0usahealthnewsreports.com 8 0 0

CircaDirect channel8health.com 313 0 1clkrd.com 428 0 0online6reports.com 961 1 0onlinenews6.com 826 4 0online6health.com 191 437 0online8report.com 252 148 0

Beony channel6reports.com 423 15 0healthnews10.com 43 0 0consumertipsdaily6.com 90 37 0rachelrayblogs.com 50 0 0consumersdigestweekly.com 580 0 0consumerstipweekly.com 85 0 0internetjobnews.net 60 0 0kljsdf.com 47 11 0

Coulomb Media news6reports.com 982 0 0fdx8news.com 90 0 0new6reports.com 86 127 0

DLXM health8news.com 51 43 0dlxm.net 84 36 0channel9newstoday.com 41 1 0channel5healthnews.com 70 66 1

Charles Dunlevy acai-berry-trial-offers.com 206 0 0colon-cleanse-trial-offers.com 44 0 0

Garrett Vaughn channel9newstoday.com 41 1 0health8news.com 51 43 0channel6reports.com 423 15 0news6report.com 259 1 0

IMM Interactive channel9healthbeat.com 131 3 0nbsnewsat6.com 51 0 0consumerproductsdaily.com 137 0 0fdxnewsat8.com 217 9 0consumeracaicleanse.com 47 47 0

Ricardo Jose Labra consumerstricksweekly.com 169 0 0consumerworkjournal.com 49 1 0

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Table 2: How consumers reach fake news sites

2010 2011

Total visits 7458 2912Organic searches 54.6% 64.0%Referred by Other domains 45.4% 36.0%

I combine this information on the identity of the fake news site with consumer browsing

data in 2010 and 2011 from comScore. comScore tracks detailed browsing and buying behav-

ior for 50,000 internet users across the United States. The panel is based on a random sample

from a cross-section of more than 2 million global Internet users who have given comScore

explicit permission to confidentially capture their Web-wide activity (Source: comScore).

Table 1 also lists the number of yearly site visits to these fake news domains using

the comScore data. Consumers can reach the fake news site either organically or be referred

there by another website/advertisement. Table 2 shows that more than 50% of traffic came in

organically, while the rest were referred by advertising platforms such as adshuffle, facebook

and rubiconproject. Since some of the organic searches might have come from users who

discovered the site earlier through a referral, I keep only the first visits per user-domain and

find the percentages to be fairly identical, i.e., over 50% of visits are organic. Figures 6 and

7 in Appendix B show examples of each of these paths. Users do not spend much time at

these fake news websites: the median number of visits per user is 1 website, and the median

duration spent is 1 minute.

Of direct relevance are the domains these fake news sites referred users to. The comScore

data not only records the domain visited, but also the referrer domain that led the user to a

given domain. We can therefore observe the set of product domains that were using the fake

news sites as referrers. Extracting all domains the fake news sites referred users to, gives

us a set of 191 product domains including sites such as fibradetox.com, getslimpackage.com

and tryacaiberrypure.com. Some of these sites are normal domains such as accuweather.com,

live.com and newsvine.com. I conduct the analysis excluding these normal domains, resulting

in 174 product domains that likely used affiliate marketers to advertise their products via

fake news. I use the term “product domains” or “merchants” to refer to the websites these

fake news sites referred consumers to.

I assume these product domains were operational after the FTC order in April 2011.

This assumption is valid because the FTC order only required the affiliate marketers to shut

down their news website operations. The merchants were not ordered to shutdown (their

investigation followed subsequently, and were required to shutdown much later, starting

in December 2011) However, to be precise, I also restrict analysis to the set of merchants

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mentioned in the FTC documents resulting in a smaller set of 38 product domains. To ensure

the product domains were operational post FTC-order, I further restrict attention to those

that received at least 1 consumer visit post-April 2011, resulting in a much smaller subset

of 8 merchants. Note this is an extremely conservative measure as comScore keeps track of

only a sample of consumers and it is extremely likely that all 38 sites had visits but are just

not visible in our sample.

There are three ways consumers can reach these product domains: 1) directly, 2) referrals

through the fake news sites or 3) referral through other regular advertisements. These three

possible paths are depicted in Figure 1. Direct visits occur when the consumer either directly

types the product domain’s address into the search bar or reaches the product site through

an organic search. Referrals through fake news sites occur when the consumer clicks on a

domain such as Channel6HealthBeat.com, and then clicks on a link to the product domain

present on that fake news site. Referrals through regular advertisements occur when a

consumer clicks on an ad such as LeanSpaAcai.com and then reaches the product domain.

The question this paper asks, is what happens when the fake news path is shutdown - do

consumers no longer find the product domains, or do they merely substitute to finding these

sites through regular advertisements. The former if true, implies a treatment effect of the

fake news sites, i.e., consumers find the product domains because of the fake news sites. The

latter if true, implies a selection effect, i.e., consumers interested in these weight-loss sites

will do so through other means, and the fake news sites was just one way of doing so.

Table 3 shows the percentage of visits coming from each of these three different paths for

all domains (All), as well as restricting attention to those sites cited by the FTC in the court

documents (Cited by FTC) and those that are definitely active after the FTC shutdown order

(Active). This table shows that fake news sites form a small portion, about 0.8-7.5% of the

way consumers reach these product domains. Because attribution is difficult: subsequent

visits could have been informed by previous visits, I keep only the first visit per individual

per domain. The last three columns of Table 3 show that fake news sites are still a small

portion of how consumers reach the domains. Regular advertisements seem to be the most

common way of how consumers reach these domains. The magnitude of this statistic is in

line with the extant literature, for example, Allcott and Gentzkow (2017) estimate that the

average US adult saw about one fake news story in the months before the election, Guess et

al (2018) estimate that about 1 in 4 Americans visited a fake news website from October -

November 2016, and that fake news consumption is concentrated in a small subset of people.

To show that after the FTC order, the fake news websites indeed discontinue operation,

Figure 2 records the site visits across all fake news websites at the monthly level. This figure

provides evidence that the fake news sites were inoperational after April 2011.

6

Figure 1: Various ways consumers can reach the product domain: 1) Directly/Organicsearches 2) Fake News ads 3) Regular ads

Table 3: How do consumers reach product domains

All visits First visits

All Cited Active All Cited Activeby FTC by FTC

Total visits per consumer-domain-year-month 2.35 1.20 1.18 1 1 1

Direct 38.0% 67.1% 64.8% 43.7% 64.8% 63.7%Referred by fake news 0.8% 7.5% 4.4% 2.3% 8.9% 5.3%Referred by Other 61.2% 25.4% 30.8% 54.1% 26.2% 31.0%

N obs 156,480 10,481 3,962 116,823 9,770 3,632N individual 55,745 7,903 3,418 55,745 7,903 3,418

Note: (All) refers to all domains that were referred to by the fake news sites, (Cited by FTC) restrictsattention to those sites cited in the FTC court documents, (Active) refers to those that are definitely activeafter the FTC shutdown order in the comScore data.

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Note: Red line indicates the date of the FTC shutdown order

Figure 2: Fake News domains’ visit counts

3 Empirical Evidence

Using the FTC order date as an event, I run a before-after regression on all product domains

that were referred to by the fake news sites. In this regression, I also compare this estimate

to a placebo date. Finally, I plan to add a control group of product domains which will

provide a differences in differences estimate.

yijt = βpre1 × PreFTCt + βpost

1 × PostFTCt

+ βpre2 × PreCtrlt + βpost

2 × PostCtrlt

+ αY + αj + αi + εijt

(1)

Here yijt is the number of visits individual user i made to product domain j in month

t.PreFTC and PostFTC represent the 3-months before and after the FTC shutdown,

PreCtrl and PostCtrl represent the 3-months before and after a placebo date which was

chosen to be 1 year before the FTC shutdown order. αY is the year fixed-effect that allows

for any yearly trends, αj is the domain fixed-effect that allows for the fact that some domains

have more visits than others, αi is the individual fixed effect.

An observation in this regression is an individual-domain-year-month. The comScore

dataset records only sessions of active browsing. To allow for no-visits, I expand the dataset

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so that every individual has an observation for every month of the data for every domain

ever visited, accounting for zero visit counts when there was no visitation to domains. This

expansion allows the individual’s search behavior to change, i.e., she might visit fewer do-

mains after the shutdown. She might also stop visiting weight-loss related websites causing

the market for such sites to shrink. Not accounting for the “zero visits” would result in an

incorrect estimate. Some sites that are referred from the fake news domains include “nor-

mal” sites such as accuweather.com and live.com and a few legitimate ad networks such as

crwdcntrl.net. To measure the impact on only the fake product domains, I eliminate such

sites2 for the regression analysis specified in Equation 1.

Table 4 presents the results of this regression. Compared to the PreFTC period, the

product domains see 0.113 fewer visits after the fake news sites were shutdown. To ensure

this is not a time-trend, the same difference for the Placebo year is 0.032 site visits. The

difference-in-difference,(βpost1 − βpre

1

)−(βpost2 − βpre

2

), is 0.081 site visits and is a statistically

significant drop. To provide further context, the average number of site visits per individual-

domain-year-month is 0.13, making this drop an economically meaningful one.

Table 4: Difference in Domain Visits after Fake News Sites were Shutdown

coeff t-stat

Post FTC βpost1 0.024 18.05

Pre FTC βpre1 0.136 30.43

Difference, FTC X (Post-Pre) βpost1 − βpre

1 -0.113 -24.11

Post Placebo βpost2 -0.110 -16.74

Pre Placebo βpre2 -0.079 -10.74

Difference, Placebo X (Post-Pre) βpost2 − βpre

2 -0.032 -3.21

Difference-in-difference estimate -0.081 -7.42N obs 2,811,129N id 55,745Fixed effects Year

DomainIndividual

Cluster Individual

2In two robustness checks, I include 1) all legitimate sites and 2) only the legitimate ad networks in theregression and find the results hold. This inclusion is done to allow for the scenario where the productdomains use legitimate sites/ad networks to advertise their products, which is possible.

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3.1 Mechanism

The above results show that visitations to product domain sites dropped after the FTCs

efforts to halt these fake news operations. While visitations to the domains from the fake

news sites have to go to zero (by definition, since the fake news sites do not exist anymore),

it is not clear whether direct visits to these sites go up or down post-shutdown. Similarly, we

do not know whether referrals coming in through other ads/sources such as facebook go up

or down. Knowing the total impact is crucial, because if consumers find their way to these

product domains through other means, then the impact of the policy (i.e., the shutdown) is

unclear.

To this end, I classify visits as “Referred via Fake News”, “Referred via Other Sources”,

and “Direct”. The total visits to a product domain consists of one of these three forms of

visits. Table 5 presents the regression results by this classification. As expected, referrals

via fake news sites (mechanistically) drop. Direct visits drop after the fake news shutdown,

suggesting there is a treatment effect of shutting down the fake news sites. Moreover, the

magnitude of the drop is almost similar to the total drop in site visits, implying that almost

all the decline is coming from a drop in direct visits to the product domains. Interestingly,

referrals from other sites (regular ads) do not change and continue to remain the same as in

the placebo year. This could imply that there is little spillover between the fake news site

shutdown and regular ad traffic.

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Table 5: Mechanism: Which source changed after fake news shutdown

(1) (2) (3)

Total Direct Referred via Referred viaFake News Other

coeff t-stat coeff t-stat coeff t-stat coeff t-stat

Post FTC βpost1 0.024 18.05 0.016 17.73 0.000 -7.03 0.008 9.89

Pre FTC βpre1 0.136 30.43 0.092 37.56 0.002 20.25 0.042 11.88

Difference, FTC X Post-Pre βpost1 − βpre

1 -0.113 -24.11 -0.076 -28.93 -0.002 -21.35 -0.035 -9.52

Post Placebo βpost2 -0.110 -16.74 -0.005 -4.61 0.000 -4.10 -0.105 -16.93

Pre Placebo βpre2 -0.079 -10.74 -0.010 -8.11 0.001 9.25 -0.070 -10.12

Difference, Placebo X Post-Pre βpost2 − βpre

2 -0.032 -3.21 0.005 2.70 -0.001 -9.95 -0.035 -3.76

Difference-in-difference estimateFTC-Placebo X Post-Pre -0.081 -7.42 -0.080 -25.73 -0.001 -4.58 0.000 0.022

N obs 2,811,129N id 55,745

Fixed effects YearDomainIndividual

Cluster Individual

Accounting for Wrongful Attribution: Using Only First Visits

Next, for each domain I keep only the first visit. This corrects for any wrongful attribution,

where an individual might have been referred to a domain by a fake news website and she

subsequently visits the site organically. The second visit should not be counted as an organic

visit, if the fake news site was the reason she discovered this site in the first place. Table

6 presents the results of this analysis. Interestingly, relative to the placebo year, site visits

to the product domains seem to be on the rise. The drop in total visits in 2011 after the

shutdown is 0.031, but in 2010 the drop was 0.042. Moreover, this rise is largely attributable

to the regular advertisements. Both referrals via fake news (2) and direct searches (1) are

on the decline relative to the placebo year, but there is an uptake in referrals to these

domains coming from regular advertisements. Although there is a drop in Referrals from

Other Sources (-0.008), this drop is lower than the drop in the Placebo Year (-0.034) which

suggests that there might have been an upsurge in first visits coming through other regular

advertisements after the shutdown of the fake news sites. This pattern suggests that there is

substitution to other channels, i.e., there is an element of selection: those who want to find

these weight-loss sites will find their way through other means.

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Table 6: Difference in Domain Visits after Fake News Sites were Shutdown: Keeping onlythe first visit per domain

(1) (2) (3)

Total Direct Referred via Referred viaFake News Other

coeff t-stat coeff t-stat coeff t-stat coeff t-stat

Post FTC βpost1 0.005 15.66 0.002 10.40 0.000 -7.09 0.003 13.04

Pre FTC βpre1 0.036 67.39 0.022 60.73 0.002 20.73 0.012 36.55

Difference, FTC X Post-Pre βpost1 − βpre

1 -0.031 -49.25 -0.020 -48.42 -0.002 -21.85 -0.008 -20.98

Post Placebo βpost2 0.001 2.54 0.000 -0.53 0.000 -4.00 0.002 5.62

Pre Placebo βpre2 0.044 66.38 0.007 18.23 0.001 9.12 0.035 78.47

Difference, Placebo X Post-Pre βpost2 − βpre

2 -0.042 -51.59 -0.007 -14.16 -0.001 -9.76 -0.034 -61.86

Difference-in-difference estimateFTC-Placebo X Post-Pre 0.012 11.38 -0.013 -19.21 -0.001 -5.22 0.025 37.42

N obs 2,810,441N id 55,745

Fixed effects YearDomainIndividual

Cluster Individual

3.2 Robustness checks

Accounting for the possibility that some Regular Ads were also shutdown

It is possible that some of the “regular ads” were also shutdown by the FTC during the

investigation period. As an example, the FTC issued a civil investigative demand (CID) to

Facebook asking for documents relevant to the fake news site“new6reports.com”. Facebook’s

declaration stated that the advertiser associated with this domain had initiated other ad

campaigns, and after the receipt of the Civil Investigative Demand, Facebook had disabled

these ads. If regular advertisements were also shutdown, then the drop estimated in Tables

5 and 6 would be an overestimate because the drop occurs mechanistically via the shutdown

of the regular ads. I therefore exclude such advertisers from the analysis, and conduct a

robustness check. To do so, I classify as Regular Ads only those that remained active post-

April 2011, i.e., after the shutdown order.

Table 7 reports the estimates of the drop in visits occurring via regular ad referrals

after the shutdown order. The results in this table are consistent with the previous results,

referrals via regular ads either remain unchanged or increase after the shutdown, providing

evidence of a selection effect.

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Table 7: Changes in referrals via regular ads

All visits First visits

coeff t-stat coeff t-stat

Post FTC βpost1 0.005 8.61 0.002 11.36

Pre FTC βpre1 0.023 10.77 0.004 25.04

Difference, FTC X Post-Pre βpost1 − βpre

1 -0.019 -8.43 -0.003 -12.08

Post Placebo βpost2 -0.073 -15.70 -0.001 -2.94

Pre Placebo βpre2 -0.049 -9.82 0.017 59.21

Difference, Placebo X Post-Pre βpost2 − βpre

2 -0.023 -3.43 -0.018 -52.45

Difference-in-difference estimateFTC-Placebo X Post-Pre 0.004 0.62 0.015 36.76

N obs 2,811,129 2,810,441N id 55,745 55,745

Fixed effects YearDomainIndividual

Cluster Individual

Note: Dependant variable is the visits to product domains arising from referrals viaregular advertisements.

Accounting for the possibility that merchants’ sites were also shutdown

Here, I check for the possibility that the merchants’ sites, i.e., domains actually selling the

products in question were shut down (either by the FTC, or pre-empted by the shut down

of their affiliate marketers). In order to do so, I first keep only the domains that are cited

in the FTC court dockets. Further, I restrict attention to those sites that have at least

one visit in the comScore data in the post-shutdown period. Table 8 presents the results

of this analysis, indicating that the drop in site visits exists even for those sites that were

active after the shutdown order. The difference in site visits in the months following the

shutdown, relative to the months before is -0.080 site visits. The same difference in the

placebo year is 0.015 visits, indicating a decline in site visits of the order of 0.095 site visits.

These number although small are economically meaningful, because they are evaluated at

the individual-domain-month level, where the average visit is 0.05 site visits.

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Table 8: Robustness Check: Keeping only those merchants that were cited by FTC,and were active post-shutdown

Cited Activeby FTC

coeff t-stat coeff t-stat

Post FTC βpost1 -0.001 -0.41 0.007 1.16

Pre FTC βpre1 0.080 27.01 0.087 18.46

Difference, FTC X Post-Pre βpost1 − βpre

1 -0.081 -21.55 -0.080 -10.30

Post Placebo βpost2 -0.035 -14.99 0.019 4.99

Pre Placebo βpre2 -0.009 -3.00 0.004 1.07

Difference, Placebo X Post-Pre βpost2 − βpre

2 -0.026 -6.74 0.015 2.75

Difference-in-difference estimateFTC-Placebo X Post-Pre -0.055 -10.24 -0.095 -10.02N obs 234,496 87,184N id 7,903 3,418Fixed effects Year

DomainIndividual

Cluster Individual

Note: Dependant variable is the total visits to product domains. (Cited by FTC) restricts attention tothose sites cited in the FTC court documents, (Active) refers to those that are definitely active afterthe FTC shutdown order in the comScore data.

Accounting for PR effect of the FTC press-release

The decline in direct visits to the product domains could be caused by negative publicity asso-

ciated with the FTC press-release, which was picked up by major news outlets. If consumers

become skeptical of the product domains that used these fake news style advertisements,

they are less likely to visit the domains. In other words, the reason for the observed em-

pirical decline is confounded between stoppage of the fake news ads and negative publicity.

To rule out negative publicity, I focus on the 3 months prior to the FTC press-release. The

FTC conducts an investigation before the public announcement, and sites have the option

of shutting down during this investigation period.

Four sites that appear to have shutdown prior to the FTC press-release are consumertips-

daily6.com, health8news.com and online6health.com. These sites had a substantial number

of visits in January 2011 and February 2011, but no visits March 2011 onward. Therefore,

it is likely these sites were inoperative following March 2011. Looking at the product do-

mains these sites referred consumers to, in March vs. February/January 2011 will give us

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the impact of the shutdown of this fake news website. More importantly, since the fake news

site shutdown prior to April 2011, any effect is not confounded with the PR effect of the

press-release which occurred more than a month later in April 2011. Table 9 presents the

results of this regression.

Table 9: Robustness Check: Accounting for PR effect of FTC press release

coeff t-stat

Post FTC βpost1 0.387 8.53

Pre FTC βpre1 0.772 9.63

Difference, FTC X Post-Pre βpost1 − βpre

1 -0.385 -4.17

Post Placebo βpost2 -0.884 -14.64

Pre Placebo βpre2 -0.892 -14.78

Difference, Placebo X Post-Pre βpost2 − βpre

2 0.008 0.09

Difference-in-difference estimate -0.393 -3.13N obs 149,209N id 5,697Fixed effects Year

DomainIndividual

Cluster Individual

4 Conclusion

This paper examines the role of fake news as advertisements, and finds that fake news can

cause increased interest in product domains. The shutdown of fake news seems to have

diverted some traffic to regular advertisements indicating that the two are substitutes at

least to some extent. Direct visits to the product domains using fake news websites dropped

significantly. While the current analysis cannot separately identify if the drop comes directly

from the absence of fake news or from any bad press surrounding these products during the

FTC investigation, this paper is one step toward understanding the impact of fake news on

consumers’ purchase decisions.

15

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A Fake News Article Example

Source: FTC Document, Declaration of Douglas McKenneyAccessed from: FTC v. Beony International LLC, Attachment C, Bloomberg Law

Figure 3: Example of a Fake News Article from consumertipsdaily6.com (Page 1)

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Source: FTC Document, Declaration of Douglas McKenneyAccessed from: FTC v. Beony International LLC, Attachment C, Bloomberg Law

Figure 4: Example of a Fake News Article from consumertipsdaily6.com (Page 2)

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Source: FTC Document, Declaration of Loretta Kraus, Coulomb Media, Inc., et al.,https://www.ftc.gov/sites/default/files/documents/cases/2011/04/110419coulomb-kraus-pt1.pdf

Figure 5: Example of a Fake News Article from consumerhealthwarning.com (Page 1)

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B How do consumers reach fake news sites

Source: FTC v Circa Direct court document, Exhibit 5Investigator’s search for commonalities, in this case “this stuff truley”, resulted in many fake news sitesappearing organically.

Figure 6: Organic links to fake news sites

Source: FTC v Ambervine court document, McKenney Attachment BInvestigator’s search for “acai berry” resulted in sponsored ads for fake news sites

Figure 7: Sponsored Ads linking to fake news sites

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