Extended Data Fig. 2 | Distribution of sharing intentions in studies 3 and 4, by condition and headline veracity. Whereas Fig. 2 discretizes the sharing intention variable for ease of interpretation such that all 'unlikely' responses are scored as 0 and all 'likely' responses are scored as 1, here the full distributions are shown. The regression models use these non-discretized values. Extended Data Fig. 3 | Distribution of sharing intentions in study 5, by condition and headline veracity. Whereas Fig. 2 discretizes the sharing intention variable for ease of interpretation such that all 'unlikely' responses are scored as 0 and all 'likely' responses are scored as 1, here the full distributions are shown. The regression models use these non-discretized values.

The spread of false and misleading news content on social media is of great societal concern. Why do people share such content, and what can be done about it? In a first survey experiment (N=1,015), we demonstrate a disconnect between accuracy judgments and sharing intentions: even though true headlines are rated as much more accurate than false headlines, headline veracity has little impact on sharing. Although this may seem to indicate that people share inaccurate content because they care more about furthering their political agenda than they care about truth, we propose an alternative attentional account: Most people do not want to spread misinformation, but the social media context focuses their attention on factors other than truth and accuracy. Indeed, when directly asked, most participants say it is important to only share news that is accurate. Accordingly, across four survey experiments (total N=3,485) and a digital field experiment on Twitter in which we messaged users who had previously shared news from websites known for publishing misleading content (N=5,379), we find that inducing people to think about the concept of accuracy increases the quality of the news they subsequently share. Together, these results challenge the narrative that people no longer care about accuracy. Instead, the results support our inattention-based account wherein people fail to implement their preference for accuracy due to attentional constraints. Furthermore, our research provides evidence for scalable anti-misinformation interventions that are easily implementable by social media platforms.

No abstract

There is an increasing imperative for psychologists and other behavioral scientists to understand how people behave on social media. However, it is often very difficult to execute experimental research on actual social media platforms, or to link survey responses to online behavior in order to perform correlational analyses. Thus, there is a natural desire to use selfreported behavioral intentions in standard survey studies to gain insight into online behavior. But are such hypothetical responses hopelessly disconnected from actual sharing decisions? Or are online survey samples via sources such as Amazon Mechanical Turk (MTurk) so different from the average social media user that the survey responses of one group give little insight into the on-platform behavior of the other? Here we investigate these issues by examining 67 pieces of political news content. We evaluate whether there is a meaningful relationship between (i) the level of sharing (tweets and retweets) of a given piece of content on Twitter, and (ii) the extent to which individuals (total N = 993) in online surveys on MTurk reported being willing to share that same piece of content. We found that the same news headlines that were more likely to be hypothetically shared on MTurk were also shared more frequently by Twitter users, r = .44. For example, across the observed range of MTurk sharing fractions, a 20 percentage point increase in the fraction of MTurk participants who reported being willing to share a news headline on social media was associated with 10x as many actual shares on Twitter. We also found that the correlation between sharing and various features of the headline was similar using both MTurk and Twitter data. These findings suggest that self-reported sharing intentions collected in online surveys are likely to provide some meaningful insight into what content would actually be shared on social media.