Journal of Economic Perspectives—Volume 31, Number 2—Spring 2017—Pages 211–236
Social Media and Fake News in the 2016
Election
Hunt Allcott and Matthew Gentzkow
A
merican democracy has been repeatedly buffeted by changes in media technology. In the 19th century, cheap newsprint and improved presses allowed
partisan newspapers to expand their reach dramatically. Many have argued
that the effectiveness of the press as a check on power was significantly compromised as a result (for example, Kaplan 2002). In the 20th century, as radio and then
television became dominant, observers worried that these new platforms would
reduce substantive policy debates to sound bites, privilege charismatic or “telegenic”
candidates over those who might have more ability to lead but are less polished, and
concentrate power in the hands of a few large corporations (Lang and Lang 2002;
Bagdikian 1983). In the early 2000s, the growth of online news prompted a new set
of concerns, among them that excess diversity of viewpoints would make it easier
for like-minded citizens to form “echo chambers” or “filter bubbles” where they
would be insulated from contrary perspectives (Sunstein 2001a, b, 2007; Pariser
2011). Most recently, the focus of concern has shifted to social media. Social media
platforms such as Facebook have a dramatically different structure than previous
media technologies. Content can be relayed among users with no significant third
party filtering, fact-checking, or editorial judgment. An individual user with no
track record or reputation can in some cases reach as many readers as Fox News,
CNN, or the New York Times.
■
Hunt Allcott is Associate Professor of Economics, New York University, New York City,
New York. Matthew Gentzkow is Professor of Economics, Stanford University, Stanford,
California. Both authors are Research Associates, National Bureau of Economic Research,
Cambridge, Massachusetts.
†
For supplementary materials such as appendices, datasets, and author disclosure statements, see the
article page at
https://doi.org/10.1257/jep.31.2.211
doi=10.1257/jep.31.2.211
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Journal of Economic Perspectives
Following the 2016 election, a specific concern has been the effect of false
stories—“fake news,” as it has been dubbed—circulated on social media. Recent
evidence shows that: 1) 62 percent of US adults get news on social media (Gottfried
and Shearer 2016); 2) the most popular fake news stories were more widely shared
on Facebook than the most popular mainstream news stories (Silverman 2016);
3) many people who see fake news stories report that they believe them (Silverman
and Singer-Vine 2016); and 4) the most discussed fake news stories tended to favor
Donald Trump over Hillary Clinton (Silverman 2016). Putting these facts together,
a number of commentators have suggested that Donald Trump would not have
been elected president were it not for the influence of fake news (for examples, see
Parkinson 2016; Read 2016; Dewey 2016).
Our goal in this paper is to offer theoretical and empirical background to
frame this debate. We begin by discussing the economics of fake news. We sketch
a model of media markets in which firms gather and sell signals of a true state of
the world to consumers who benefit from inferring that state. We conceptualize
fake news as distorted signals uncorrelated with the truth. Fake news arises in equilibrium because it is cheaper to provide than precise signals, because consumers
cannot costlessly infer accuracy, and because consumers may enjoy partisan news.
Fake news may generate utility for some consumers, but it also imposes private and
social costs by making it more difficult for consumers to infer the true state of the
world—for example, by making it more difficult for voters to infer which electoral
candidate they prefer.
We then present new data on the consumption of fake news prior to the election. We draw on web browsing data, a new 1,200-person post-election online survey,
and a database of 156 election-related news stories that were categorized as false by
leading fact-checking websites in the three months before the election.
First, we discuss the importance of social media relative to sources of political
news and information. Referrals from social media accounted for a small share of
traffic on mainstream news sites, but a much larger share for fake news sites. Trust in
information accessed through social media is lower than trust in traditional outlets.
In our survey, only 14 percent of American adults viewed social media as their “most
important” source of election news.
Second, we confirm that fake news was both widely shared and heavily tilted
in favor of Donald Trump. Our database contains 115 pro-Trump fake stories that
were shared on Facebook a total of 30 million times, and 41 pro-Clinton fake stories
shared a total of 7.6 million times.
Third, we provide several benchmarks of the rate at which voters were exposed
to fake news. The upper end of previously reported statistics for the ratio of page
visits to shares of stories on social media would suggest that the 38 million shares
of fake news in our database translates into 760 million instances of a user clicking
through and reading a fake news story, or about three stories read per American
adult. A list of fake news websites, on which just over half of articles appear to be false,
received 159 million visits during the month of the election, or 0.64 per US adult. In
our post-election survey, about 15 percent of respondents recalled seeing each of 14
Hunt Allcott and Matthew Gentzkow
213
major pre-election fake news headlines, but about 14 percent also recalled seeing a
set of placebo fake news headlines—untrue headlines that we invented and that never
actually circulated. Using the difference between fake news headlines and placebo
headlines as a measure of true recall and projecting this to the universe of fake news
articles in our database, we estimate that the average adult saw and remembered 1.14
fake stories. Taken together, these estimates suggest that the average US adult might
have seen perhaps one or several news stories in the months before the election.
Fourth, we study inference about true versus false news headlines in our survey
data. Education, age, and total media consumption are strongly associated with
more accurate beliefs about whether headlines are true or false. Democrats and
Republicans are both about 15 percent more likely to believe ideologically aligned
headlines, and this ideologically aligned inference is substantially stronger for
people with ideologically segregated social media networks.
We conclude by discussing the possible impacts of fake news on voting patterns
in the 2016 election and potential steps that could be taken to reduce any negative
impacts of fake news. Although the term “fake news” has been popularized only
recently, this and other related topics have been extensively covered by academic
literatures in economics, psychology, political science, and computer science. See
Flynn, Nyhan, and Reifler (2017) for a recent overview of political misperceptions.
In addition to the articles we cite below, there are large literatures on how new information affects political beliefs (for example, Berinsky 2017; DiFonzo and Bordia
2007; Taber and Lodge 2006; Nyhan, Reifler, and Ubel 2013; Nyhan, Reifler, Richey,
and Freed 2014), how rumors propagate (for example, Friggeri, Adamic, Eckles,
and Cheng 2014), effects of media exposure (for example, Bartels 1993, DellaVigna
and Kaplan 2007, Enikolopov, Petrova, and Zhuravskaya 2011, Gerber and Green
2000, Gerber, Gimpel, Green, and Shaw 2011, Huber and Arceneaux 2007,
Martin and Yurukoglu 2014, and Spenkuch and Toniatti 2016; and for overviews,
DellaVigna and Gentzkow 2010, and Napoli 2014), and ideological segregation in
news consumption (for example, Bakshy, Messing, and Adamic 2015; Gentzkow and
Shapiro 2011; Flaxman, Goel, and Rao 2016).
Background: The Market for Fake News
Definition and History
We define “fake news” to be news articles that are intentionally and verifiably
false, and could mislead readers. We focus on fake news articles that have political
implications, with special attention to the 2016 US presidential elections. Our definition includes intentionally fabricated news articles, such as a widely shared article
from the now-defunct website denverguardian.com with the headline, “FBI agent
suspected in Hillary email leaks found dead in apparent murder-suicide.” It also
includes many articles that originate on satirical websites but could be misunderstood as factual, especially when viewed in isolation on Twitter or Facebook feeds.
For example, in July 2016, the now-defunct website wtoe5news.com reported that
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Journal of Economic Perspectives
Pope Francis had endorsed Donald Trump’s presidential candidacy. The WTOE 5
News “About” page disclosed that it is “a fantasy news website. Most articles on wtoe5news.com are satire or pure fantasy,” but this disclaimer was not included in the
article. The story was shared more than one million times on Facebook, and some
people in our survey described below reported believing the headline.
Our definition rules out several close cousins of fake news: 1) unintentional
reporting mistakes, such as a recent incorrect report that Donald Trump had
removed a bust of Martin Luther King Jr. from the Oval Office in the White House;
2) rumors that do not originate from a particular news article; 1 3) conspiracy theories (these are, by definition, difficult to verify as true or false, and they are typically
originated by people who believe them to be true); 2 4) satire that is unlikely to be
misconstrued as factual; 5) false statements by politicians; and 6) reports that are
slanted or misleading but not outright false (in the language of Gentzkow, Shapiro,
and Stone 2016, fake news is “distortion,” not “filtering”).
Fake news and its cousins are not new. One historical example is the “Great
Moon Hoax” of 1835, in which the New York Sun published a series of articles about
the discovery of life on the moon. A more recent example is the 2006 “Flemish
Secession Hoax,” in which a Belgian public television station reported that the
Flemish parliament had declared independence from Belgium, a report that a
large number of viewers misunderstood as true. Supermarket tabloids such as the
National Enquirer and the Weekly World News have long trafficked in a mix of partially
true and outright false stories.
Figure 1 lists 12 conspiracy theories with political implications that have circulated over the past half-century. Using polling data compiled by the American
Enterprise Institute (2013), this figure plots the share of people who believed each
statement is true, from polls conducted in the listed year. For example, substantial
minorities of Americans believed at various times that Franklin Roosevelt had prior
knowledge of the Pearl Harbor bombing, that Lyndon Johnson was involved in the
Kennedy assassination, that the US government actively participated in the 9/11
bombings, and that Barack Obama was born in another country.
The long history of fake news notwithstanding, there are several reasons to
think that fake news is of growing importance. First, barriers to entry in the media
industry have dropped precipitously, both because it is now easy to set up websites
and because it is easy to monetize web content through advertising platforms.
Because reputational concerns discourage mass media outlets from knowingly
reporting false stories, higher entry barriers limit false reporting. Second, as we
discuss below, social media are well-suited for fake news dissemination, and social
1
Sunstein (2007) defines rumors as “claims of fact—about people, groups, events, and institutions—that
have not been shown to be true, but that move from one person to another, and hence have credibility
not because direct evidence is available to support them, but because other people seem to believe
them.”
2
Keeley (1999) defines a conspiracy theory as “a proposed explanation of some historical event (or events)
in terms of the significant causal agency of a relatively small group of persons—the conspirators––acting
in secret.”
Social Media and Fake News in the 2016 Election
215
Figure 1
Share of Americans Believing Historical Partisan Conspiracy Theories
1975: The assassination of Martin Luther King
was the act of part of a large conspiracy
1991: President Franklin Roosevelt knew Japanese
plans to bomb Pearl Harbor but did nothing
1994: The Nazi extermination of millions
of Jews did not take place
1995: FBI deliberately set the Waco fire
in which the Branch Davidians died
1995: US government bombed the government building
in Oklahoma City to blame extremist groups
1995: Vincent Foster, the former aide to
President Bill Clinton, was murdered
1999: The crash of TWA Flight 800 over Long Island
was an accidental strike by a US Navy missile
2003: Lyndon Johnson was involved in the
assassination of John Kennedy in 1963
2003: Bush administration purposely misled the public
about evidence that Iraq had banned weapons
2007: US government knew the 9/11 attacks were
coming but consciously let them proceed
2007: US government actively planned or
assisted some aspects of the 9/11 attacks
2010: Barack Obama was born in another country
0
10
20
30
40
50
60
Share of people who believe it is true (%)
Note: From polling data compiled by the American Enterprise Institute (2013), we selected all
conspiracy theories with political implications. This figure plots the share of people who report
believing the statement listed, using opinion polls from the date listed.
media use has risen sharply: in 2016, active Facebook users per month reached 1.8
billion and Twitter’s approached 400 million. Third, as shown in Figure 2A, Gallup
polls reveal a continuing decline of “trust and confidence” in the mass media “when
it comes to reporting the news fully, accurately, and fairly.” This decline is more
marked among Republicans than Democrats, and there is a particularly sharp
drop among Republicans in 2016. The declining trust in mainstream media could
be both a cause and a consequence of fake news gaining more traction. Fourth,
Figure 2B shows one measure of the rise of political polarization: the increasingly
negative feelings each side of the political spectrum holds toward the other. 3 As we
3
The extent to which polarization of voters has increased, along with the extent to which it has been
driven by shifts in attitudes on the right or the left or both, are widely debated topics. See Abramowitz
and Saunders (2008), Fiorina and Abrams (2008), Prior (2013), and Lelkes (2016) for reviews.
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Journal of Economic Perspectives
Figure 2
Trends Related to Fake News
A: Trust in Mainstream Media
80
Percent Great deal/Fair amount
Democrats
60
Overall
40
Republicans
20
0
1998
2002
2006
2010
2014
B: Feeling Thermometer toward Other Political Party
Feeling thermometer
(0 = least positive, 100 = most)
50
Republicans’ feeling thermometer
toward Democratic Party
40
30
Democrats’ feeling thermometer
toward Republican Party
20
1980
1984
1988
1992
1996
2000
2004
2008
2012
Note: Panel A shows the percent of Americans who say that they have “a great deal” or “a fair
amount” of “trust and confidence” in the mass media “when it comes to reporting the news fully,
accurately, and fairly,” using Gallup poll data reported in Swift (2016). Panel B shows the average
“feeling thermometer” (with 100 meaning “very warm or favorable feeling” and 0 meaning “very
cold or unfavorable feeling”) of Republicans toward the Democratic Party and of Democrats
toward the Republican Party, using data from the American National Election Studies (2012).
Hunt Allcott and Matthew Gentzkow
217
discuss below, this could affect how likely each side is to believe negative fake news
stories about the other.
Who Produces Fake News?
Fake news articles originate on several types of websites. For example, some
sites are established entirely to print intentionally fabricated and misleading
articles, such as the above example of denverguardian.com. The names of these
sites are often chosen to resemble those of legitimate news organizations. Other
satirical sites contain articles that might be interpreted as factual when seen out
of context, such as the above example of wtoe5news.com. Still other sites, such as
endingthefed.com, print a mix between factual articles, often with a partisan slant,
along with some false articles. Websites supplying fake news tend to be short-lived,
and many that were important in the run-up to the 2016 election no longer exist.
Anecdotal reports that have emerged following the 2016 election provide
a partial picture of the providers behind these sites. Separate investigations by
BuzzFeed and the Guardian revealed that more than 100 sites posting fake news
were run by teenagers in the small town of Veles, Macedonia (Subramanian 2017).
Endingthefed.com, a site that was responsible for four of the ten most popular
fake news stories on Facebook, was run by a 24-year-old Romanian man (Townsend
2016). A US company called Disinfomedia owns many fake news sites, including
NationalReport.net, USAToday.com.co, and WashingtonPost.com.co, and its owner
claims to employ between 20 and 25 writers (Sydell 2016). Another US-based
producer, Paul Horner, ran a successful fake news site called National Report for
years prior to the election (Dewey 2014). Among his most-circulated stories was
a 2013 report that President Obama used his own money to keep open a Muslim
museum during the federal government shutdown. During the election, Horner
produced a large number of mainly pro-Trump stories (Dewey 2016).
There appear to be two main motivations for providing fake news. The first
is pecuniary: news articles that go viral on social media can draw significant advertising revenue when users click to the original site. This appears to have been the
main motivation for most of the producers whose identities have been revealed. The
teenagers in Veles, for example, produced stories favoring both Trump and Clinton
that earned them tens of thousands of dollars (Subramanian 2017). Paul Horner
produced pro-Trump stories for profit, despite claiming to be personally opposed to
Trump (Dewey 2016). The second motivation is ideological. Some fake news providers
seek to advance candidates they favor. The Romanian man who ran endingthefed.
com, for example, claims that he started the site mainly to help Donald Trump’s
campaign (Townsend 2016). Other providers of right-wing fake news actually say
they identify as left-wing and wanted to embarrass those on the right by showing that
they would credulously circulate false stories (Dewey 2016; Sydell 2016).
A Model of Fake News
How is fake news different from biased or slanted media more broadly? Is
it an innocuous form of entertainment, like fictional films or novels? Or does it
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Journal of Economic Perspectives
have larger social costs? To answer these questions, we sketch a model of supply
and demand for news loosely based on a model developed formally in Gentzkow,
Shapiro, and Stone (2016).
There are two possible unobserved states of the world, which could represent
whether a left- or right-leaning candidate will perform better in office. Media firms
receive signals that are informative about the true state, and they may differ in the
precision of these signals. We can also imagine that firms can make costly investments to increase the accuracy of these signals. Each firm has a reporting strategy
that maps from the signals it receives to the news reports that it publishes. Firms
can either decide to report signals truthfully, or alternatively to add bias to reports.
Consumers are endowed with heterogeneous priors about the state of the world.
Liberal consumers’ priors hold that the left-leaning candidate will perform better in
office, while conservative consumers’ priors hold that the right-leaning candidate
will perform better. Consumers receive utility through two channels. First, they want
to know the truth. In our model, consumers must choose an action, which could
represent advocating or voting for a candidate, and they receive private benefits if
they choose the candidate they would prefer if they were fully informed. Second,
consumers may derive psychological utility from seeing reports that are consistent
with their priors. Consumers choose the firms from which they will consume news
in order to maximize their own expected utility. They then use the content of the
news reports they have consumed to form a posterior about the state of the world.
Thus, consumers face a tradeoff: they have a private incentive to consume precise
and unbiased news, but they also receive psychological utility from confirmatory
news.
After consumers choose their actions, they may receive additional feedback
about the true state of the world—for example, as a candidate’s performance is
observed while in office. Consumers then update their beliefs about the quality of
media firms and choose which to consume in future periods. The profits of media
firms increase in their number of consumers due to advertising revenue, and media
firms have an incentive to build a reputation for delivering high levels of utility
to consumers. There are also positive social externalities if consumers choose the
higher-quality candidate.
In this model, two distinct incentives may lead firms to distort their reports in the
direction of consumers’ priors. First, when feedback about the true state is limited,
rational consumers will judge a firm to be higher quality when its reports are closer
to the consumers’ priors (Gentzkow and Shapiro 2006). Second, consumers may
prefer reports that confirm their priors due to psychological utility (­Mullainathan
and Shleifer 2005). Gentzkow, Shapiro, and Stone (2016) show how these incentives can lead to biased reporting in equilibrium, and apply variants of this model to
understand outcomes in traditional “mainstream” media.
How would we understand fake news in the context of such a model? Producers
of fake news are firms with two distinguishing characteristics. First, they make no
investment in accurate reporting, so their underlying signals are uncorrelated with
the true state. Second, they do not attempt to build a long-term reputation for
Social Media and Fake News in the 2016 Election
219
quality, but rather maximize the short-run profits from attracting clicks in an initial
period. Capturing precisely how this competition plays out on social media would
require extending the model to include multiple steps where consumers see “headlines” and then decide whether to “click” to learn more detail. But loosely speaking,
we can imagine that such firms attract demand because consumers cannot distinguish them from higher-quality outlets, and also because their reports are tailored
to deliver psychological utility to consumers on either the left or right of the political spectrum.
Adding fake news producers to a market has several potential social costs. First,
consumers who mistake a fake outlet for a legitimate one have less-accurate beliefs
and are worse off for that reason. Second, these less-accurate beliefs may reduce
positive social externalities, undermining the ability of the democratic process to
select high-quality candidates. Third, consumers may also become more skeptical
of legitimate news producers, to the extent that they become hard to distinguish
from fake news producers. Fourth, these effects may be reinforced in equilibrium by
supply-side responses: a reduced demand for high-precision, low-bias reporting will
reduce the incentives to invest in accurate reporting and truthfully report signals.
These negative effects trade off against any welfare gain that arises from consumers
who enjoy reading fake news reports that are consistent with their priors.
Real Data on Fake News
Fake News Database
We gathered a database of fake news articles that circulated in the three
months before the 2016 election, using lists from three independent third parties.
First, we scraped all stories from the Donald Trump and Hillary Clinton tags on
Snopes (snopes.com), which calls itself “the definitive Internet reference source for
urban legends, folklore, myths, rumors, and misinformation.” Second, we scraped
all stories from the 2016 presidential election tag from PolitiFact (politifact.com),
another major fact-checking site. Third, we use a list of 21 fake news articles that
had received significant engagement on Facebook, as compiled by the news outlet
BuzzFeed (Silverman 2016).4 Combining these three lists, we have a database of
156 fake news articles. We then gathered the total number of times each article was
shared on Facebook as of early December 2016, using an online content database
called BuzzSumo (buzzsumo.com). We code each article’s content as either proClinton (including anti-Trump) or pro-Trump (including anti-Clinton).
This list is a reasonable but probably not comprehensive sample of the major
fake news stories that circulated before the election. One measure of comprehensiveness is to look at the overlap between the lists of stories from Snopes, PolitiFact,
and BuzzFeed. Snopes is our largest list, including 138 of our total of 156 articles. As
4
Of these 21 articles, 12 were fact-checked on Snopes. Nine were rated as “false,” and the other three
were rated “mixture,” “unproven,” and “mostly false.”
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Journal of Economic Perspectives
a benchmark, 12 of the 21 articles in the BuzzFeed list appear in Snopes, and 4 of
the 13 articles in the PolitiFact appear in Snopes. The lack of perfect overlap shows
that none of these lists is complete and suggests that there may be other fake news
articles that are omitted from our database.
Post-Election Survey
During the week of November 28, 2016, we conducted an online survey of
1208 US adults aged 18 and over using the SurveyMonkey platform. The sample
was drawn from SurveyMonkey’s Audience Panel, an opt-in panel recruited from
the more than 30 million people who complete SurveyMonkey surveys every month
(as described in more detail at https://www.surveymonkey.com/mp/audience/).
The survey consisted of four sections. First, we acquired consent to participate
and a commitment to provide thoughtful answers, which we hoped would improve
data quality. Those who did not agree were disqualified from the survey. Second,
we asked a series of demographic questions, including political affiliation before
the 2016 campaign, vote in the 2016 presidential election, education, and race/
ethnicity. Third, we asked about 2016 election news consumption, including time
spent on reading, watching, or listening to election news in general and on social
media in particular, and the most important source of news and information about
the 2016 election. Fourth, we showed each respondent 15 news headlines about the
2016 election. For each headline, we asked, “Do you recall seeing this reported or
discussed prior to the election?” and “At the time of the election, would your best
guess have been that this statement was true?” We also received age and income
categories, gender, and census division from profiling questions that respondents
had completed when they first started taking surveys on the Audience panel. The
survey instrument can be accessed at https://www.surveymonkey.com/r/RSYD75P.
Each respondent’s 15 news headlines were randomly selected from a list of
30 news headlines, six from each of five categories. Within each category, our list
contains an equal split of pro-Clinton and pro-Trump headlines, so 15 of the 30 articles favored Clinton, and the other 15 favored Trump. The first category contains
six fake news stories mentioned in three mainstream media articles (one in the New
York Times, one in the Wall Street Journal, and one in BuzzFeed) discussing fake news
during the week of November 14, 2016. The second category contains the four
most recent pre-election headlines from each of Snopes and PolitiFact deemed to
be unambiguously false. We refer to these two categories individually as “Big Fake”
and “Small Fake,” respectively, or collectively as “Fake.” The third category contains
the most recent six major election stories from the Guardian’s election timeline.
We refer to these as “Big True” stories. The fourth category contains the two most
recent pre-election headlines from each of Snopes and PolitiFact deemed to be
unambiguously true. We refer to these as “Small True” stories. Our headlines in
these four categories appeared on or before November 7.
The fifth and final category contains invented “Placebo” fake news headlines,
which parallel placebo conspiracy theories employed in surveys by Oliver and Wood
(2014) and Chapman University (2016). As we explain below, we include these
Hunt Allcott and Matthew Gentzkow
221
Placebo headlines to help control for false recall in survey responses. We invented
three damaging fake headlines that could apply to either Clinton or Trump, then
randomized whether a survey respondent saw the pro-Clinton or pro-Trump
version. We experimented with several alternative placebo headlines during a pilot
survey, and we chose these three because the data showed them to be approximately equally believable as the “Small Fake” stories. (We confirmed using Google
searches that none of the Placebo stories had appeared in actual fake news articles.) Online Appendix Table 1, available with this article at this journal’s website
(http://e-jep.org), lists the exact text of the headlines presented in the survey. The
online Appendix also presents a model of survey responses that makes precise the
conditions under which differencing with respect to the placebo articles leads to
valid inference.
Yeager et al. (2011) and others have shown that opt-in internet panels such
as ours typically do not provide nationally representative results, even after
reweighting. Notwithstanding, reweighting on observable variables such as education and internet usage can help to address the sample selection biases inherent in
an opt-in internet-based sampling frame. For all results reported below, we reweight
the online sample to match the nationwide adult population on ten characteristics that we hypothesized might be correlated with survey responses, including
income, education, gender, age, ethnicity, political party affiliation, and how often
the respondent reported consuming news from the web and from social media. The
online Appendix includes summary statistics for these variables; our unweighted
sample is disproportionately well-educated, female, and Caucasian, and those who
rely relatively heavily on the web and social media for news. The Appendix also
includes additional information on data construction.
Social Media as a Source of Political Information
The theoretical framework we sketched above suggests several reasons why social
media platforms may be especially conducive to fake news. First, on social media,
the fixed costs of entering the market and producing content are vanishingly small.
This increases the relative profitability of the small-scale, short-term strategies often
adopted by fake news producers, and reduces the relative importance of building a
long-term reputation for quality. Second, the format of social media—thin slices of
information viewed on phones or news feed windows—can make it difficult to judge
an article’s veracity. Third, Bakshy, Messing, and Adamic (2015) show that Facebook
friend networks are ideologically segregated—among friendships between people
who report ideological affiliations in their profiles, the median share of friends with
the opposite ideology is only 20 percent for liberals and 18 percent for conservatives—and people are considerably more likely to read and share news articles that
are aligned with their ideological positions. This suggests that people who get news
from Facebook (or other social media) are less likely to receive evidence about the
true state of the world that would counter an ideologically aligned but false story.
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Journal of Economic Perspectives
Figure 3
Share of Visits to US News Websites by Source
Direct
browsing
48.7
Top news sites
Other
links
10.6
Search
engines
30.6
Social
media
10.1
Other links
Direct
browsing
30.5
Fake news sites
0
20
Other
links
5.7
Search
engines
22.0
40
60
Weighted average of percentage (%)
Social
media
41.8
80
100
Note: This figure presents the share of traffic from different sources for the top 690 US news
websites and for 65 fake news websites. “Other links” means impressions that were referred from
sources other than search engines and social media. “Direct browsing” means impressions that did
not have a referral source. Sites are weighted by number of monthly visits. Data are from Alexa.
One way to gauge the importance of social media for fake news suppliers is to
measure the source of their web traffic. Each time a user visits a webpage, that user
has either navigated directly (for example, by typing www.wsj.com into a browser)
or has been referred from some other site. Major referral sources include social
media (for example, clicking on a link in the Facebook news feed) and search
engines (for example, searching for “Pope endorsed Trump?” on Google and
clicking on a search result). Figure 3 presents web traffic sources for the month
around the 2016 US presidential election (late October through late November)
from Alexa (alexa.com), which gathers data from browser extensions installed
on people’s computers as well as from measurement services offered to websites.
These data exclude mobile browsing and do not capture news viewed directly on
social media sites, for example, when people read headlines within Facebook or
Twitter news feeds.
The upper part of the graph presents referral sources for the top 690 US news
sites, as ranked by Alexa. The lower part of the graph presents web traffic sources for
a list of 65 major fake news sites, which we gathered from lists compiled by Zimdars
(2016) and Brayton (2016). For the top news sites, social media referrals represent
only about 10 percent of total traffic. By contrast, fake news websites rely on social
Social Media and Fake News in the 2016 Election
223
media for a much higher share of their traffic. This demonstrates the importance
of social media for fake news providers. While there is no definitive list of fake news
sites, and one might disagree with the inclusion or exclusion of particular sites in
this list of 65, this core point about the importance of social media for fake news
providers is likely to be robust.
A recent Pew survey (Gottfried and Shearer 2016) finds that 62 percent of US
adults get news from social media. To the extent that fake news is socially costly and
fake news is prevalent on social media, this statistic could appear to be cause for
concern. Of this 62 percent, however, only 18 percent report that they get news from
social media “often,” 26 percent do so “sometimes,” and 18 percent do so “hardly
ever.” By comparison, the shares who “often” get news from local television, national
broadcast television, and cable television are 46 percent, 30 percent, and 31 percent
respectively. Moreover, only 34 percent of web-using adults trust the information
they get from social media “some” or “a lot.” By contrast, this share is 76 percent for
national news organizations and 82 percent for local news organizations.
The results of our post-election survey are broadly consistent with this picture.
For the month before the 2016 election, our respondents report spending 66
minutes per day reading, watching, or listening to election news. (Again, these and
all other survey results are weighted for national representativeness.) Of this, 25
minutes (38 percent) was on social media. Our survey then asked, “Which of these
sources was your most important source of news and information about the 2016
election?” The word “important” was designed to elicit a combination of consumption frequency and trust in information. Figure 4 presents responses. In order, the
four most common responses are cable TV, network TV, websites, and local TV.
Social media is the fifth most common response, with 14 percent of US adults listing
social media as their most “important” news source.
Taken together, these results suggest that social media have become an important but not dominant source of political news and information. Television remains
more important by a large margin.
Partisanship of Fake News
In our fake news database, we record 41 pro-Clinton (or anti-Trump) and 115
pro-Trump (or anti-Clinton) articles, which were shared on Facebook a total of
7.6 million and 30.3 million times, respectively. Thus, there are about three times
more fake pro-Trump articles than pro-Clinton articles, and the average pro-Trump
article was shared more on Facebook than the average pro-Clinton article. To be
clear, these statistics show that more of the fake news articles on these three factchecking sites are right-leaning. This could be because more of the actual fake news
is right-leaning, or because more right-leaning assertions are forwarded to and/or
reported by fact-checking sites, or because the conclusions that fact-checking sites
draw have a left-leaning bias, or some combination. Some anecdotal reports support
the idea that the majority of election-related fake news was pro-Trump: some fake
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Journal of Economic Perspectives
Figure 4
Most Important Source of 2016 Election News
19.2%
Network TV
23.5%
Cable TV
14.8% Website
Local TV
14.5%
Radio
Social
media
6.2%
Print
8.0%
13.8%
Notes: Our post-election survey asked, “Which of these sources was your most important source
of news and information about the 2016 election?” This figure plots responses. Observations are
weighted for national representativeness.
news providers reportedly found higher demand for pro-Trump (or anti-Clinton)
fake news, and responded by providing more of it (Sydell 2016).
There could be several possible explanations for a preponderance of proTrump fake news. The more marked decline of trust in the mainstream media
among Republicans shown in Figure 2 could have increased their relative demand
for news from nontraditional sources, as could a perception that the mainstream
media tended to favor Clinton. Pro-Trump (and anti-Clinton) storylines may have
simply been more compelling than pro-Clinton (and anti-Trump) storylines due to
particulars of these candidates, perhaps related to the high levels of media attention
that Trump received throughout the campaign. Or, it could theoretically be that
Republicans are for some reason more likely to enjoy or believe fake news.
Some prior evidence argues against the last hypothesis. McClosky and Chong
(1985) and Uscinski, Klofstad, and Atkinson (2016) find that people on the left
and right are equally disposed to conspiratorial thinking. Furthermore, Bakshy,
Messing, and Adamic (2015) find that conservatives are actually exposed to more
cross-cutting news content than liberals, which could help conservatives to be better
at detecting partisan fake news. Below, we present further evidence on this hypothesis from our survey.
Hunt Allcott and Matthew Gentzkow
225
Exposure to Fake News
How much fake news did the typical voter see in the run-up to the 2016 election? While there is a long literature measuring media exposure (for example, Price
and Zaller 1993), fake news presents a particular challenge: much of its circulation
is on Facebook (and other social media) news feeds, and these data are not public.
We provide three benchmarks for election-period fake news exposure, which we
report as average exposure for each of the 248 million American adults.
First, we can use prior evidence to predict the number of times the articles
in our database were read based on the number of times they were shared. The
corporate website of Eventbrite (2012) reports that links to its events on Facebook
generate 14 page visits per share. A blog post by Jessica Novak (undated) reports
that for a set of “top performing” stories on Facebook the ratio of visits to shares
was also 14. Zhao, Wang, Tao, Ma, and Guan (2013) report that the ratio of views to
shares for videos on the Chinese social networking site Renren ranges from 3 to 8.
Based on these very rough reference points, we consider a ratio of 20 page visits per
share as an upper bound on the plausible range. This implies that the 38 million
shares of fake news in our database translate into 760 million page visits, or about
three visits per US adult.
Second, we can use web browsing data to measure impressions on fake news
websites. For the month around the 2016 election, there were 159 million impressions on the 65 websites in the bottom part of Figure 3, or 0.64 impressions per
adult. This is dwarfed by the 3 billion impressions on the 665 top news websites over
the same period. Furthermore, not all content on these 65 sites is false: in a random
sample of articles from these sites, we categorized just under 55 percent as false,
either because the claim was refuted by a mainstream news site or fact-checking
organization, or because the claim was not covered on any other sites despite being
important enough that it would have been covered on other sites if it were true.
When comparing these first two approaches to estimating election-period fake news
exposure, remember that the first approach uses cumulative Facebook shares as
of early December 2016 for fake news articles that were fact-checked in the three
months before the election, while the second approach uses web traffic from a one
month period between late October to late November 2016.
Third, we can use our post-election survey to estimate the number of articles
respondents saw and remembered. The survey gave respondents 15 news headlines—three headlines randomly selected from each of the five categories detailed
earlier—and asked if they recalled seeing the headline (“Do you recall seeing this
reported or discussed prior to the election?”) and if they believed it (“At the time of
the election, would your best guess have been that this statement was true?”).
Figure 5 presents the share of respondents that recalled seeing (left bar) and
seeing and believing (right bar) headlines, averaging responses across all the headlines within each of our main categories. Rates of both seeing and believing are
much higher for true than fake stories, and they are substantially higher for the
“Big True” headlines (the major headlines leading up to the election) than for the
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Journal of Economic Perspectives
Figure 5
Percent of US Adult Population that Recall Seeing or that Believed Election
News
Percent of US Adult population
80
Recalled seeing
Recalled seeing and believed
60
40
20
0
Big true
Small true
Fake
Placebo
Notes: In our post-election survey, we presented 15 headlines. For each headline, the survey asked
whether respondents recall seeing the headline (“Do you recall seeing this reported or discussed
before the election?”) and whether they believed it (“At the time of the election, would your best
guess have been that this statement was true?”). The left bars present the share of respondents who
recall seeing the headlines in each category, and the right bars present the share of respondents
who recall seeing and believed the headlines. “Big True” headlines are major headlines leading up
to the election; “Small True” headlines are the minor fact-checked headlines that we gathered from
Snopes and PolitiFact. The Placebo fake news headlines were made-up for the research and never
actually circulated. Observations are weighted for national representativeness.
“Small True” headlines (the minor fact-checked headlines that we gathered from
Snopes and PolitiFact). The Placebo fake news articles, which never actually circulated, are approximately equally likely to be recalled and believed as the Fake news
articles which did actually circulate. This implies that there is a meaningful rate of
false recall of articles that people never actually saw, which could cause the survey
measure to significantly overstate true exposure. On the other hand, people likely
forgot some of the Fake articles that they were actually exposed to, which causes the
survey responses to understate true exposure.
In summary, one can think of recalled exposure as determined both by actual
exposure and by the headline’s perceived plausibility—people might think that if a
headline is plausible, they probably saw it reported somewhere. Then, we show that
if the Placebo headlines are equally plausible as the Fake headlines, the difference
between recall of Fake and Placebo headlines represents the rate of true exposure that
was remembered. The Appendix available online with this paper at http://e-jep.org
presents additional theoretical and empirical discussion of false recall in our data.
Social Media and Fake News in the 2016 Election
227
After weighting for national representativeness, 15 percent of survey respondents recalled seeing the Fake stories, and 8 percent both recalled seeing the story
and said they believed it.5 By comparison, about 14 percent of people report seeing
the placebo stories, and about 8 percent report seeing and believing them. We estimate that the average Fake headline was 1.2 percentage points more likely to be
seen and recalled than the average Placebo headline, and the 95 percent confidence interval allows us to exclude differences greater than 2.9 percent.
We can use these results to provide a separate estimate of fake news exposure.
The average Fake article that we asked about in the post-election survey was shared
0.386 million times on Facebook. If the average article was seen and recalled by
1.2 percent of American adults, this gives (0.012 recalled exposure)/(0.386 million
shares) ≈ 0.03 chance of a recalled exposure per million Facebook shares. Given
that the Fake articles in our database had 38 million Facebook shares, this implies
that the average adult saw and remembered 0.03/million × 38 million ≈ 1.14 fake
news articles from our fake news database.
All three approaches suggest that election-period fake news exposure was on
the order of one or perhaps several articles read per adult. We emphasize several
important caveats. First, each of these measures excludes some forms of exposure
that could have been influential. All of them exclude stories or sites omitted from
our database. Estimated page visits or impressions exclude cases in which users saw
a story within their Facebook news feed but did not click through to read it. Our
survey-based recall measure excludes stories that users saw but did not remember,
and may be subject to other biases associated with survey-based estimates of media
exposure (Bartels 1993; Prior 2009; Guess 2015).
Who Believes Fake News?
It is both privately and socially valuable when people can infer the true state
of the world. What factors predict the ability to distinguish between real and fake
news? This analysis parallels a literature in political science measuring and interpreting correlates of misinformation, including Lewandowsky, Oberauer, and
Gignac (2013), Malka, Krosnick, and Langer (2009), and Oliver and Wood (2014).
We construct a variable Cia, that takes value 1 if survey respondent i correctly
identifies whether article a is true or false, 0.5 if respondent i is “not sure,” and value
0 otherwise. For example, if headline a is true, then Cia takes value 1 if person i
responded “Yes” to “would your best guess have been that this statement was true?”;
0.5 if person i responded “Not sure”; and 0 if person i responded “No.” We use Cia
5
These shares are broadly consistent with the results of a separate survey conducted by Silverman and
Singer-Vine (2016): for a set of five fake news stories, they find that the share of respondents who have
heard them ranges from 10 to 22 percent and the share who rate them as “very accurate” ranges from
28 to 49 percent.
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Journal of Economic Perspectives
as the dependent variable and a vector Xi of individual characteristics in a linear
regression:
Cia = α1Xi + α0 + εia.
Table 1 reports results. Column 1 includes only false articles (both Fake and
Placebo), and focuses only on party affiliation; the omitted category is Independents.
In these data, it is indeed true that Republicans were statistically less likely than
Democrats to report that they (correctly) did not believe a false article. Column 2
includes only true articles (both Big True and Small True categories). This suggests
that Republicans are also more likely than Democrats to correctly believe articles
that were true ( p = 0.124). These results suggest that in our data, Republicans were
not generally worse at inference: instead, they tended to be more credulous of both
true and false articles. Of course, it is possible that this is simply an artifact of how
different respondents interpreted the survey design. For example, it could be that
Republicans tended to expect a higher share of true headlines in our survey, and
thus were less discerning.
Another possible explanation is that the differences between parties hide
other factors associated with party affiliation. Columns 3 and 4 test this possibility,
including a vector of additional covariates. The differences between the Democrat
and Republican indicator variables are relatively robust. Column 5 includes all articles, which weights true and false articles by the proportions in our survey sample.
Given that our survey included a large proportion of fake articles that Republicans
were less likely to recognize as false, Democrats are overall more likely to correctly
identify true versus false articles. Three correlations tend to be statistically significant: people who spend more time consuming media, people with higher education,
and older people have more accurate beliefs about news. As with Republicans relative to Democrats, people who report that social media were their most important
sources of election news were more likely both to correctly believe true headlines
and to incorrectly believe false headlines.
The association of education with correct beliefs should be highlighted. Flynn,
Nyhan, and Reifler (2017) argue that education could have opposing effects on
political misperceptions. On the one hand, education should increase people’s
ability to discern fact from fiction. On the other hand, in the presence of motivated
reasoning, education gives people better tools to counterargue against incongruent information. To the extent that the association in our data is causal, it would
reinforce many previous arguments that the social return to education includes
cognitive abilities that better equip citizens to make informed voting decisions. For
example, Adam Smith (1776) wrote, “The more [people] are instructed, the less
liable they are to the delusions of enthusiasm and superstition, which, among ignorant nations, frequently occasion the most dreadful disorders.”
A common finding in the survey literature on rumors, conspiracy theories,
and factual beliefs is that partisan attachment is an important predictor of beliefs
(for example, Oliver and Wood 2014; Uscinski, Klofstad, and Atkinson 2016).
Hunt Allcott and Matthew Gentzkow
229
Table 1
What Predicts Correct Beliefs about News Headlines?
(1)
(2)
Democrat
0.029
(0.020)
–0.004
(0.023)
Republican
–0.024
(0.024)
0.040
(0.027)
(3)
(4)
(5)
0.028
(0.019)
–0.010
(0.021)
0.015
(0.013)
–0.037*
(0.020)
0.021
(0.023)
–0.018
(0.014)
ln(Daily media time)
–0.002
(0.007)
0.042***
(0.008)
0.013***
(0.004)
Social media most
important
–0.066***
(0.025)
0.065***
(0.024)
–0.023
(0.016)
Use social media
0.014
(0.030)
–0.023
(0.038)
0.002
(0.019)
Social media
ideological segregation
–0.027
(0.036)
0.028
(0.046)
–0.008
(0.024)
Education
0.014***
(0.004)
0.004
(0.004)
0.011***
(0.003)
Undecided
–0.011
(0.017)
0.006
(0.022)
–0.005
(0.013)
Age
0.002***
(0.000)
0.000
(0.001)
0.002***
(0.000)
N
12,080
6,040
12,080
6,040
18,120
p-value
(Democrat = Republican)
Articles in sample
0.029
0.124
0.004
0.207
0.035
False
True
False
True
All
Note: This table presents estimates of a regression of a dependent variable measuring correct
beliefs about headlines on individual characteristics. Columns 1 and 3 include only false
headlines, columns 2 and 4 contain only true headlines, and column 5 contains all headlines.
All columns include additional demographic controls: income, race, and gender. “Social media
most important” means social media were the respondent’s most important sources of election
news. “Social media ideological segregation” is the self-reported share (from 0 to 1) of social
media friends that preferred the same presidential candidate. “Undecided” is an indicator
variable for whether the respondent decided which candidate to vote for less than three months
before the election. Observations are weighted for national representativeness. Standard errors
are robust and clustered by survey respondent.
*, **, *** indicate statistically significantly different from zero with 90, 95, and 99 percent
confidence, respectively.
For example, Republicans are more likely than Democrats to believe that President Obama was born outside the United States, and Democrats are more likely
than Republicans to believe that President Bush was complicit in the 9/11 attacks
(Cassino and Jenkins 2013). Such polarized beliefs are consistent with a Bayesian
framework, where posteriors depend partially on priors, as well as with models of
motivated reasoning (for example, Taber and Lodge 2006, or see the symposium
in the Summer 2016 issue of this journal). Either way, the ability to update one’s
priors in response to factual information is privately and socially valuable in our
model, and polarized views on factual issues can damage society’s ability to come
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Journal of Economic Perspectives
to agreement on what social problems are important and how to address them
(Sunstein 2001a, b, 2007).
Given this discussion, do we also see polarized beliefs with respect to fake news?
And if so, what factors moderate ideologically aligned inference—that is, what
factors predict a lower probability that a Republican is more likely to believe proTrump news than pro-Clinton news, or that a Democrat is more likely to believe
pro-Clinton than pro-Trump news? To gain insight into this question, we define Bia
as a measure of whether individual i believed article a, taking value 1 if “Yes,” 0.5
if “Not sure,” and 0 if “No.” We also define Di and Ri as Democrat and Republican
indicators, and Ca and Ta as indicators for whether headline a is pro-Clinton or
pro-Trump. We then run the following regression in the sample of Democrats and
Republicans, excluding Independents:
Bia = βDDiCa + βR Ri Ta + γDDi + γRRi + εia.
The first two independent variables are interaction terms; their coefficients βD
and βR measure whether a Democrat is more likely to believe a pro-Clinton headline and whether a Republican is more likely to believe a pro-Trump headline. The
second two independent variables control for how likely Democrats or Republicans
are as a group are to believe all stories. Since headlines are randomly assigned to
respondents, with equal balance of true versus false and pro-Trump versus proClinton, the estimated β parameters will measure ideologically aligned inference
Table 2 presents the results. Column 1 presents estimates of βD and βR. Democrats and Republicans, respectively, are 17.2 and 14.7 percentage points more likely
to believe ideologically aligned articles than they are to believe nonaligned articles.
Column 2 takes an intermediate step, constraining the β coefficients to be the same.
Column 3 then allows β to vary by the same vector of Xi variables as reported in
Table 1, except excluding Di to avoid collinearity. In both columns 1 and 3, any
differences between Democrats and Republicans in the magnitude of ideologically
aligned inference are not statistically significant.
Three variables are strongly correlated with ideologically aligned inference.
First, heavy media consumers are more likely to believe ideologically aligned articles. Second, those with segregated social networks are significantly more likely to
believe ideologically aligned articles, perhaps because they are less likely to receive
disconfirmatory information from their friends. The point estimate implies that a
0.1 (10 percentage point) increase in the share of social media friends that preferred
the same presidential candidate is associated with a 0.0147 (1.47 percentage point)
increase in belief of ideologically aligned headlines relative to ideologically crosscutting headlines. Third, “undecided” adults (those who did not make up their
minds about whom to vote for until less than three months before the election) are
less likely to believe ideologically aligned articles than more decisive voters. This
is consistent with undecided voters having less-strong ideologies in the first place.
Interestingly, social media use and education are not statistically significantly associated with more or less ideologically aligned inference.
Social Media and Fake News in the 2016 Election
231
Table 2
Ideological Alignment and Belief of News Headlines
(1)
Democrat × Pro-Clinton
0.172***
(0.021)
Republican × Pro-Trump
0.147***
(0.023)
Aligned
(2)
0.161***
(0.016)
(3)
0.096
(0.140)
Aligned × Republican
0.000
(0.027)
Aligned × ln(Daily media time)
0.024***
(0.009)
Aligned × Social media most important
–0.031
(0.037)
Aligned × Use social media
–0.068
(0.050)
Aligned × Social media ideological segregation
0.147***
(0.046)
Aligned × Education
–0.004
(0.007)
Aligned × Undecided
–0.099***
(0.030)
Aligned × Age
0.001
(0.001)
N
10,785
10,785
10,785
Notes: This table presents estimates of a regression of a variable measuring belief of news headlines
on the interaction of political party affiliation indicators and pro-Clinton or pro-Trump headline
indicators. The sample includes all news headlines (both true and false) but excludes survey
respondents who are Independents. “Social media most important” means social media were the
respondent’s most important sources of election news. “Social media ideological segregation” is
the self-reported share (from 0 to 1) of social media friends that preferred the same presidential
candidate. “Undecided” is an indicator variable for whether the respondent decided which
candidate to vote for less than three months before the election. Observations are weighted
for national representativeness. Standard errors are robust and clustered by survey respondent.
*, **, ***: statistically significantly different from zero with 90, 95, and 99 percent confidence,
respectively.
One caveat to these results is that ideologically aligned inference may be exaggerated by respondents’ tendency to answer expressively or to want to “cheerlead”
for their party (Bullock, Gerber, Hill, and Huber 2015; Gerber and Huber 2009;
Prior, Sood, and Khanna 2015). Partisan gaps could be smaller in a survey with
strong incentives for correct answers.
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Journal of Economic Perspectives
Conclusion
In the aftermath of the 2016 US presidential election, it was alleged that fake
news might have been pivotal in the election of President Trump. We do not provide
an assessment of this claim one way or another.
That said, the new evidence we present clarifies the level of overall exposure
to fake news, and it can give some sense of how persuasive fake news would need
to have been to have been pivotal. We estimate that the average US adult read and
remembered on the order of one or perhaps several fake news articles during the
election period, with higher exposure to pro-Trump articles than pro-Clinton articles. How much this affected the election results depends on the effectiveness of
fake news exposure in changing the way people vote. As one benchmark, Spenkuch
and Toniatti (2016) show that exposing voters to one additional television campaign
ad changes vote shares by approximately 0.02 percentage points. This suggests that
if one fake news article were about as persuasive as one TV campaign ad, the fake
news in our database would have changed vote shares by an amount on the order
of hundredths of a percentage point. This is much smaller than Trump’s margin of
victory in the pivotal states on which the outcome depended.
Of course there are many reasons why a single fake news story could have been
more effective than a television commercial. If it were true that the Pope endorsed
Donald Trump, this fact would be significantly more surprising—and probably
move a rational voter’s beliefs by more as a result—than the information contained
in a typical campaign ad. Moreover, as we emphasize above, there are many ways in
which our estimates could understate true exposure. We only measure the number
of stories read and remembered, and the excluded stories seen on news feeds but
not read, or read but not remembered, could have had a large impact. Our fake
news database is incomplete, and the effect of the stories it omits could also be
significant.
We also note that there are several ways in which this back-of-the-envelope
calculation is conservative, in the sense that it could overstate the importance of
fake news. We consider the number of stories voters read regardless of whether they
believed them. We do not account for diminishing returns, which could reduce
fake news’ effect to the extent that a small number of voters see a large number of
stories. Also, this rough calculation does not explicitly take into account the fact
that a large share of pro-Trump fake news is seen by voters who are already predisposed to vote for Trump—the larger this selective exposure, the smaller the impact
we would expect of fake news on vote shares.
To the extent that fake news imposes social costs, what can and should be done?
In theory, a social planner should want to address the market failures that lead to
distortions, which would take the form of increasing information about the state of
the world and increasing incentives for news consumers to infer the true state of the
world. In practice, social media platforms and advertising networks have faced some
pressure from consumers and civil society to reduce the prevalence of fake news on
their systems. For example, both Facebook and Google are removing fake news sites
Hunt Allcott and Matthew Gentzkow
233
from their advertising platforms on the grounds that they violate policies against
misleading content (Wingfield, Isaac, and Benner 2016). Furthermore, Facebook
has taken steps to identify fake news articles, flag false articles as “disputed by 3rd
party fact-checkers,” show fewer potentially false articles in users’ news feeds, and
help users avoid accidentally sharing false articles by notifying them that a story is
“disputed by 3rd parties” before they share it (Mosseri 2016). In our theoretical
framework, these actions may increase social welfare, but identifying fake news sites
and articles also raises important questions about who becomes the arbiter of truth.
■ We thank David Deming, Brendan Nyhan, Craig Silverman, Aaron Smith, Joe Uscinski,
David Vannette, and many other colleagues for helpful conversations and feedback. We are
grateful to Chuan Yu and Nano Barahona for research assistance, and we thank Stanford
University for financial support. Our survey was determined to be exempt from human
subjects review by the NYU and Stanford Institutional Review Boards.
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