Less than you think: Prevalence and predictors of fake news dissemination on Facebook

One 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 implications 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 evidence 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 journalistic 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 network (3), but what is the role of social transmission—in particular, social sharing—in 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.

It 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%) respondents 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 overall. 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 content, 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 Republicans, 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 sample 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 pattern is evident for ideology (Fig. 2C): Conservatives, especially those identifying 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 produced during the 2016 campaign, and of the tendency of respondents 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 news–spreading domains to their friends. Thus, it is not the case that what explains fake news sharing is simply that some respondents “will share anything.” 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 characteristic of respondents, a notable finding in Fig. 2B is the clear association 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 analysis 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 (e^1.9 ≈ 6.69, e^1.9−1.079 ≈ 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 model—sex, race, education, and income—have 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 dependent 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.

Using 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 news–spreading domains is perhaps expected. More puzzling is the independent role of age: Holding constant 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 scientists. Political scientists tend to favor explanations based on stable, 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 correlated with those demographics. Yet, when an empirical relationship such as the one documented here emerges, we are challenged to view demographic traits not as controls to be ignored but as central explanatory factors above and beyond the constructs standard in the literature (12). This is especially the case with age, as the largest generation 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 studies, it is possible that an entire cohort of Americans, now in their 60s and beyond, lacks the level of digital media literacy necessary to reliably 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). Building 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 designed to maximize engagement. Research on age and digital media literacy often focuses on youth skills acquisition and the divide between “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 compounded by the tendency to use social endorsements as credibility cues (19). If true, this would imply a growing impact as more Americans from older age groups join online social communities. A second possibility, drawn from cognitive and social psychology, suggests a general effect of aging on memory. Under this account, memory deteriorates with age in a way that particularly undermines resistance to “illusions of truth” and other effects related to belief persistence and the availability heuristic, especially in relation to source cues (20–22). The severity of these effects would theoretically increase with the complexity of the information environment and the prevalence of misinformation.

We cannot definitively rule out the possibility that there is an omitted variable biasing our estimates, although we have included controls for many individual-level characteristics theoretically related to acceptance of misinformation and willingness to share content online. Even if our models are correctly specified, we use observational data that cannot provide causal evidence on the determinants of fake news sharing. This study takes advantage of a novel and powerful new dataset combining survey responses and digital trace data that overcomes well-known biases in sample selection and self-reports of online behavior (8, 9). However, we are still limited in our ability to collect these data unobtrusively. Despite our high response rate, half of our respondents with Facebook accounts opted not to share their profile data with us. Any inferences are therefore limited insofar as the likelihood of sharing data is correlated with other characteristics of interest.

In addition, while our approach allows for enhanced measurement of online sharing behavior, we lack data on the composition of respondents’ Facebook News Feeds. It is possible, for instance, that very conservative Facebook users were exposed to more fake news articles in their networks and that the patterns we observe are not due to differential willingness to believe or share false content across the political spectrum. While other evidence suggests the limits of the “echo chambers” narrative (23), we cannot rule out this possibility. Similarly, it may be the case that the composition of older Facebook users’ News Feeds differs in systematically important ways from that of younger users; while we lack the data in the present work to test this proposition, future research with access to these data could prove illuminating. These concerns aside, the evidence we have presented is strongly suggestive of an emerging relationship between not only ideological affiliation but also age and the still-rare tendency to spread misinformation to one’s friends on social media platforms. If the association with age holds in future studies, there are a whole host of questions about the causes, mechanisms, and ramifications of the relationship that researchers should explore. First, how much of the effect can be attributed to lack of digital or media literacy as opposed to explanations rooted in cognitive abilities and memory? Answering this question will require developing measures of digital literacy that can be behaviorally validated. Second, what is the role of the (currently) unobserved News Feed and social network environment on people’s tendency to see, believe, and spread dubious content? How are consumption and spreading, if at all, related? How does social trust over networks mediate the relationship between age and the sharing of misinformation?

Last, if media literacy is a key explanatory factor, then what are the interventions that could effectively increase people’s ability to discern information quality in a complex, high-choice media environment replete with contradictory social and political cues? Both theory and existing curricula could serve as the basis of rigorously controlled evaluations, both online and in the classroom, which could then help to inform educational efforts targeted at people in different age groups and with varying levels of technological skill. These efforts leave open the possibility that simple interventions, perhaps even built into online social environments, could reduce the spread of misinformation by those most vulnerable to deceptive content. Developing these innovations would be further aided by increased cooperation between academic researchers and the platforms themselves (24).

This study has been approved by the New York University Institutional Review Board (IRB-12-9058 and IRB-FY2017-150). We thank S. Luks at YouGov for facilitating survey data collection and J. Ronen for development and technical assistance. Funding: This research was supported by the INSPIRE program of the NSF (Award SES-1248077). Author contributions: A.G., J.N., and J.T. designed the research. J.N. and J.T. oversaw data collection. A.G. analyzed the data and produced the figures. A.G. drafted the paper, and all authors contributed to revisions. Competing interests: J.T. received a small fee from Facebook to compensate him for time spent in organizing a 1-day conference for approximately 30 academic researchers and a dozen Facebook product managers and data scientists that was held at NYU in the summer of 2017 to discuss research related to civic engagement. He did not provide any consulting services or any advice to Facebook as part of this arrangement. He also participated in other unpaid meetings of academics researchers and Facebook product managers and data scientists to discuss topics related to social media and politics. All of these events occurred after the data collection for the current paper. All other authors declare that they have no competing interests. Data and materials availability: Counts and survey variables at the respondent level needed to replicate all tables and figures in the paper are available at doi.org/10.7910/DVN/IKDTPZ. All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors.