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Guess et al., Sci. Adv. 2019; 5 : eaau4586 9 January 2019SCIENCE ADVANCES |RESEARCH ARTICLE1 of 8SOCIAL SCIENCESLess than you think: Prevalence and predictors of fake news dissemination on FacebookAndrew Guess1*, Jonathan Nagler2, Joshua Tucker2So-called “fake news” has renewed concerns about the prevalence and effects of misinformation in political cam-paigns. Given the potential for widespread dissemination of this material, we examine the individual-level char-acteristics associated with sharing false articles during the 2016 U.S. presidential campaign. To do so, we uniquely link an original survey with respondents sharing activity as recorded in Facebook profile data. First and foremost, we find that sharing this content was a relatively rare activity. Conservatives were more likely to share articles from fake news domains, which in 2016 were largely pro-Trump in orientation, than liberals or moderates. We also find a strong age effect, which persists after controlling for partisanship and ideology: On average, users over 65 shared nearly seven times as many articles from fake news domains as the youngest age group.INTRODUCTIONOne of the most discussed phenomena in the aftermath of the 2016 U.S. presidential election was the spread and possible influence of “fake news”false or misleading content intentionally dressed up to look like news articles, often for the purpose of generating ad revenue. Scholars and commentators have raised concerns about the impli-cations of fake news for the quality of democratic discourse, as well as the prevalence of misinformation more generally (1). Some have gone so far as to assert that such content had a persuasive impact that could have affected the election outcome, although the best ev-idence suggests that these claims are farfetched (2). While evidence is growing on the prevalence (3), believability (2), and resistance to corrections (4, 5) of fake news during the 2016 campaign, less is known about the mechanisms behind its spread (6). Some of the earliest jour-nalistic accounts of fake news highlighted its popularity on social media, especially Facebook (7). Visits to Facebook appear to be much more common than other platforms before visits to fake news articles in web consumption data, suggesting a powerful role for the social net-work (3), but what is the role of social transmissionin particular, social sharingin the spread of this pernicious form of false political content? Here, we provide important new evidence complementing the small but growing body of literature on the fake news phenomenon.Data and methodOur approach allows us to provide a comprehensive observational portrait of the individual-level characteristics related to posting arti-cles from fake newsspreading domains to friends on social media. We link a representative online survey (N = 3500) to behavioral data on respondents Facebook sharing history during the campaign, avoid-ing known biases in self-reports of online activity (8, 9). Posts con-taining links to external websites are cross-referenced against lists of fake news publishers built by journalists and academics. Here, we mainly use measures constructed by reference to the list by Silverman (7), but in the Supplementary Materials, we show that the main re-sults hold when alternate lists are used, such as that used by peer- reviewed studies (2).Overall, sharing articles from fake news domains was a rare ac-tivity. We find some evidence that the most conservative users were more likely to share this contentthe vast majority of which was pro- Trump in orientationthan were other Facebook users, although this is sensitive to coding and based on a small number of respon-dents. Our most robust finding is that the oldest Americans, especially those over 65, were more likely to share fake news to their Facebook friends. This is true even when holding other characteristicsincluding education, ideology, and partisanshipconstant. No other demographic characteristic seems to have a consistent effect on sharing fake news, making our age finding that much more notable.RESULTSIt is important to be clear about how rare this behavior is on social platforms: The vast majority of Facebook users in our data did not share any articles from fake news domains in 2016 at all (Fig. 1), and as the left panel shows, this is not because people generally do not share links: While 3.4% of respondents for whom we have Facebook profile data shared 10 or fewer links of any kind, 310 (26.1%) respon-dents shared 10 to 100 links during the period of data collection and 729 (61.3%) respondents shared 100 to 1000 links. Sharing of stories from fake news domains is a much rarer event than sharing links over-all. The right panel of Fig. 1 reveals a large spike at 0, with a long tail that goes as far as 50 shares for a single Facebook user, and we see in Table 1 that over 90% of our respondents shared no stories from fake news domains. According to our main measure of fake news con-tent, 8.5% of respondents for whom we have linked Facebook data shared at least one such article to their friends. Again referencing Fig. 1, among those who shared fake news to their friends, more were Re-publicans, both in absolute (38 Republican versus 17 Democratic respondents) and in relative (18.1% of Republicans versus 3.5% of Democrats in our sample) terms.We further explore the factors that explain the variation in fake news sharing behavior. As shown in Fig. 2A, Republicans in our sam-ple shared more stories from fake news domains than Democrats; moreover, self-described independents on average shared roughly as many as Republicans (0.506 and 0.480, respectively). A similar pat-tern is evident for ideology (Fig. 2C): Conservatives, especially those 1Department of Politics and Woodrow Wilson School, Princeton University, Fisher Hall, Princeton, NJ 08544, USA. 2Wilf Family Department of Politics and Social Me-dia and Political Participation (SMaPP) Lab, New York University, 19 West 4th Street, New York, NY 10012, USA.*Corresponding author. Email: aguessprinceton.eduCopyright 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).on January 15, 2019advances.sciencemag/Downloaded from Guess et al., Sci. Adv. 2019; 5 : eaau4586 9 January 2019SCIENCE ADVANCES |RESEARCH ARTICLE2 of 8identifying as “very conservative,” shared the most articles from fake news domains. On average, a conservative respondent shared 0.75 such stories 95% confidence interval (CI), 0.537 to 0.969, and a very conservative respondent shared 1.0 (95% CI, 0.775 to 1.225). This is consistent with the pro-Trump slant of most fake news articles pro-duced during the 2016 campaign, and of the tendency of respon-dents to share articles they agree with, and thus might not represent a greater tendency of conservatives to share fake news than liberals conditional on being exposed to it (3).Figure 2D shows that, if anything, those who share the most content in general were less likely to share articles from fake newsspreading domains to their friends. Thus, it is not the case that what explains fake news sharing is simply that some respondents “will share any-thing.” These data are consistent with the hypothesis that people who share many links are more familiar with what they are seeing and are able to distinguish fake news from real news. (We note that we have no measure as to whether or not respondents know that what they are sharing is fake news.) Turning to a key demographic char-acteristic of respondents, a notable finding in Fig. 2B is the clear as-sociation between age group and the average number of articles from fake news domains shared on Facebook. Those over 65 shared an average of 0.75 fake news articles (95% CI, 0.515 to 0.977), more than twice as many as those in the second-oldest age group (0.26 articles; 95% CI, 0.206 to 0.314). Of course, age is correlated with other characteristics, including political predispositions. Thus, we turn to a multivariate anal-ysis to examine the marginal impact of individual characteristics.Table 2 shows that the age effect remains statistically significant when controlling for ideology and other demographic attributes. The association is also robust to controlling for party, as the various alternative specifications provided in the Supplementary Materials illustrate. In column 2, the coefficient on “Age: over 65” implies that being in the oldest age group was associated with sharing nearly seven times as many articles from fake news domains on Facebook as those in the youngest age group, or about 2.3 times as many as those in the next-oldest age group, holding the effect of ideology, education, and the total number of web links shared constant (e1.9 6.69, e1.91.079 2.27). This association is also found in the specifications using the alternate peer-reviewed measure (2) as a dependent variable in columns 3 and 4, with those over 65 sharing between three and four times as many fake news links as those in the youngest age group.Aside from the overall rarity of the practice, our most robust and consistent finding is that older Americans were more likely to share articles from fake news domains. This relationship holds even when we condition on other factors, such as education, party affiliation, ideological self-placement, and overall posting activity. It is robust to a wide range of strategies for measuring fake news (see Materials and Methods). Further, none of the other demographics variables in our modelsex, race, education, and incomehave anywhere close to a robust predictive effect on sharing fake news. We subject our findings to a battery of robustness tests in the Supplementary Materials. Among them, we show that model specification, other predictors such as political knowledge, and distributional assumptions about the de-pendent variable do not appear to be driving our results (tables S1 to S8 and S13). Last, we show in table S14 that, when we try to explain patterns of hard news sharing behavior using the same approach, the predictors are more varied and do not include age.Fig. 1. Distribution of total and fake news shares. (Left) Histogram of the total number of links to articles on the web shared by respondents in the sample who iden-tified as Democrats, Republicans, or independents. (Right) Stacked histogram of the number of fake news articles shared by respondents who identified as Democrats, Republicans, or independents using the measure derived from (7).Table 1. Distribution of fake news shares. 0 1 2 3 4 510 11501090 (91.5%) 63 (5.3%) 12 (1.0%) 8 (0.01%) 5 (0.01%) 9 (0.01%) 4 (0.01%)on January 15, 2019advances.sciencemag/Downloaded from Guess et al., Sci. Adv. 2019; 5 : eaau4586 9 January 2019SCIENCE ADVANCES |RESEARCH ARTICLE3 of 8DISCUSSIONUsing unique behavioral data on Facebook activity linked to individual- level survey data, we find, first, that sharing fake news was quite rare during the 2016 U.S. election campaign. This is important context given the prominence of fake news in post-election narratives about the role of social media disinformation campaigns. Aside from the relatively low prevalence, we document that both ideology and age were associated with that sharing activity. Given the overwhelming pro-Trump orientation in both the supply and consumption of fake news during that period, including via social pathways on Facebook (3), the finding that more conservative respondents were more likely to share articles from fake newsspreading domains is perhaps ex-pected. More puzzling is the independent role of age: Holding con-stant ideology, party identification, or both, respondents in each age category were more likely to share fake news than respondents in the next-youngest group, and the gap in the rate of fake news sharing between those in our oldest category (over 65) and youngest category is large and notable.These findings pose a challenge and an opportunity for social sci-entists. Political scientists tend to favor explanations based on sta-ble, deeply held partisan or ideological predispositions (10, 11). The predictive power of demographic traits evaporates when subjected to multiple regression analyses that control for other characteristics cor-related with those demographics. Yet, when an empirical relationship Fig. 2. Average number of fake news shares (and 95% CIs) using the list of domains derived from (7). (A) Party identification, (B) age group, (C) ideological self-placement, and (D) overall number of Facebook wall posts. Proportions adjusted to account for sample-matching weights derived from the third wave of the SMaPP YouGov panel survey.on January 15, 2019advances.sciencemag/Downloaded from Guess et al., Sci. Adv. 2019; 5 : eaau4586 9 January 2019SCIENCE ADVANCES |RESEARCH ARTICLE4 of 8such as the one documented here emerges, we are challenged to view demographic traits not as controls to be ignored but as central ex-planatory factors above and beyond the constructs standard in the literature (12). This is especially the case with age, as the largest gener-ation in America enters retirement at a time of sweeping demographic and technological change. Below, we suggest possible avenues for further research incorporating insights from multiple disciplines.Given the general lack of attention paid to the oldest generations in the study of political behavior thus far, more research is needed to better understand and contextualize the interaction of age and online political content. Two potential explanations warrant further investigation. First, following research in sociology and media stud-ies, it is possible that an entire cohort of Americans, now in their 60s and beyond, lacks the level of digital media literacy necessary to reli-ably determine the trustworthiness of news encountered online (13, 14). There is a well-established research literature on media literacy and its importance for navigating new media technologies (15). Build-ing on existing work (16, 17), researchers should further develop competency-based measures of digital media literacy that encompass the kinds of skills needed to identify and avoid dubious content de-signed to maximize engagement. Research on age and digital media literacy often focuses on youth skills acquisition and the divide be-tween “digital natives” and “digital immigrants” (18), but our results suggest renewed focus on the oldest age cohorts.Within this cohort, lower levels of digital literacy could be com-pounded by the tendency to use social endorsements as credibility Table 2. Determinants of fake news sharing on Facebook. Quasi-Poisson models with YouGovs sample-matching weights applied. Dependent variables are counts of fake news articles shared using
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