by H Allcott · Cited by 5269 — pdf. Novak, Jessica. No date. “Quantifying Virality: The Visits to Share Ratio.” intelligence.r29.
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Journal of Economic PerspectivesŠVolume 31, Number 2ŠSpring 2017ŠPages 211Œ236 American democracy has been repeatedly buffeted by changes in media tech-nology. 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 compro-mised 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 fitelegenicfl 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 fiecho chambersfl or fifilter bubblesfl 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 . Social Media and Fake News in the 2016 Election˜ 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. ƒ For supplementary materials such as appendices, datasets, and author disclosure statements, see the article page athttps://doi.org/10.1257/jep.31.2.211 doi=10.1257/jep.31.2.211Hunt Allcott and Matthew Gentzkow
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212 Journal of Economic Perspectives Following the 2016 election, a specific concern has been the effect of false storiesŠfifake news,fl as it has been dubbedŠcirculated 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 equi- librium 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 worldŠfor 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 elec -tion. 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 fimost importantfl 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
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Hunt Allcott and Matthew Gentzkow 213major pre-election fake news headlines, but about 14 percent also recalled seeing a set of placebo fake news headlinesŠuntrue 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 fifake newsfl 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 infor -mation 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 fifake newsfl 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 defi- nition includes intentionally fabricated news articles, such as a widely shared article from the now-defunct website denverguardian.com with the headline, fiFBI agent suspected in Hillary email leaks found dead in apparent murder-suicide.fl It also includes many articles that originate on satirical websites but could be misunder-stood 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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214 Journal of Economic Perspectives Pope Francis had endorsed Donald Trump™s presidential candidacy. The WTOE 5 News fiAboutfl page disclosed that it is fia fantasy news website. Most articles on wtoe- 5news.com are satire or pure fantasy,fl 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 theo-ries (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 fidistortion,fl not fifilteringfl). Fake news and its cousins are not new. One historical example is the fiGreat Moon Hoaxfl 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 fiFlemish Secession Hoax,fl 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 circu-lated 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 ficlaims of factŠabout people, groups, events, and institutionsŠthat 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.fl2 Keeley (1999) defines a conspiracy theory as fia proposed explanation of some historical event (or events) in terms of the significant causal agency of a relatively small group of personsŠthe conspiratorsŒŒacting in secret.fl
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Social Media and Fake News in the 2016 Election 215media 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 fitrust and confidencefl in the mass media fiwhen it comes to reporting the news fully, accurately, and fairly.fl 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.Figure 1 Share of Americans Believing Historical Partisan Conspiracy Theories 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.0102030405060Share of people who believe it is true (%)2010: Barack Obama was born in another country2007: US government actively planned orassisted some aspects of the 9/11 attacks2007: US government knew the 9/11 attacks werecoming but consciously let them proceed2003: Bush administration purposely misled the publicabout evidence that Iraq had banned weapons2003: Lyndon Johnson was involved in theassassination of John Kennedy in 19631999: The crash of TWA Flight 800 over Long Islandwas an accidental strike by a US Navy missile1995: Vincent Foster, the former aide toPresident Bill Clinton, was murdered1995: US government bombed the government buildingin Oklahoma City to blame extremist groups1995: FBI deliberately set the Waco ˜rein which the Branch Davidians died1994: The Nazi extermination of millionsof Jews did not take place1991: President Franklin Roosevelt knew Japaneseplans to bomb Pearl Harbor but did nothing1975: The assassination of Martin Luther Kingwas the act of part of a large conspiracy
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216 Journal of Economic Perspectives Figure 2 Trends Related to Fake News Note: Panel A shows the percent of Americans who say that they have fia great dealfl or fia fair amountfl of fitrust and confidencefl in the mass media fiwhen it comes to reporting the news fully, accurately, and fairly,fl using Gallup poll data reported in Swift (2016). Panel B shows the average fifeeling thermometerfl (with 100 meaning fivery warm or favorable feelingfl and 0 meaning fivery cold or unfavorable feelingfl) of Republicans toward the Democratic Party and of Democrats toward the Republican Party, using data from the American National Election Studies (2012). A:TrustinMainstreamMedia020406080Percent Great deal/Fair amount19982002200620102014OverallB:FeelingThermometertowardOtherPoliticalParty20304050Feeling thermometer (0 = least positive, 100 = most)198019841988199219962000200420082012Republicans™ feeling thermometer toward Democratic PartyDemocrats™ feeling thermometer toward Republican PartyDemocratsRepublicans
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218 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 invest – ments 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 worldŠfor 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 incen – tives can lead to biased reporting in equilibrium, and apply variants of this model to understand outcomes in traditional fimainstreamfl 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
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Social Media and Fake News in the 2016 Election 219quality, 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 fihead- linesfl and then decide whether to ficlickfl to learn more detail. But loosely speaking, we can imagine that such firms attract demand because consumers cannot distin – guish 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 polit – ical 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 fithe definitive Internet reference source for urban legends, folklore, myths, rumors, and misinformation.fl 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 pro- Clinton (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 comprehen- siveness 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 fifalse,fl and the other three were rated fimixture,fl fiunproven,fl and fimostly false.fl
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220 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, fiDo you recall seeing this reported or discussed prior to the election?fl and fiAt the time of the election, would your best guess have been that this statement was true?fl 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 arti – cles 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 fiBig Fakefl and fiSmall Fake,fl respectively, or collectively as fiFake.fl The third category contains the most recent six major election stories from the Guardian™s election timeline. We refer to these as fiBig Truefl 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 fiSmall Truefl stories. Our headlines in these four categories appeared on or before November 7. The fifth and final category contains invented fiPlacebofl 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
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Hunt Allcott and Matthew Gentzkow 221Placebo 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 approxi – mately equally believable as the fiSmall Fakefl stories. (We confirmed using Google searches that none of the Placebo stories had appeared in actual fake news arti- cles.) 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 educa – tion 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 character -istics 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 mediaŠthin slices of information viewed on phones or news feed windowsŠcan make it difficult to judge an article™s veracity. Third, Bakshy, Messing, and Adamic (2015) show that Facebook friend networks are ideologically segregatedŠamong 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 conserva – tivesŠand 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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