Fighting COVID-19 Misinformation on Social Media: Experimental Evidence for a Scalable Accuracy-Nudge Intervention

Abstract

Across two studies with more than 1,700 U.S. adults recruited online, we present evidence that people share false claims about COVID-19 partly because they simply fail to think sufficiently about whether or not the content is accurate when deciding what to share. In Study 1, participants were far worse at discerning between true and false content when deciding what they would share on social media relative to when they were asked directly about accuracy. Furthermore, greater cognitive reflection and science knowledge were associated with stronger discernment. In Study 2, we found that a simple accuracy reminder at the beginning of the study (i.e., judging the accuracy of a non-COVID-19-related headline) nearly tripled the level of truth discernment in participants' subsequent sharing intentions. Our results, which mirror those found previously for political fake news, suggest that nudging people to think about accuracy is a simple way to improve choices about what to share on social media.

Keywords: decision making; open data; open materials; policy making; preregistered; reflectiveness; social cognition; social media.

Fig. 1.

Results from Study 1: percentage of “yes” responses for each combination of headline veracity (true vs. false) and condition (accuracy = “To the best of your knowledge, is the claim in the above headline accurate?” vs. sharing = “Would you consider sharing this story online (for example, through Facebook or Twitter)?”). Error bars indicate 95% confidence intervals.

Fig. 2.

Results from Study 2: percentage of headlines participants said they would be likely to share, separately for each combination of headline veracity (true vs. false) and condition (control vs. treatment). For this visualization, we discretize sharing intentions using the scale midpoint (i.e., 1–3 = 0, 4–6 = 1) to give a more easily interpretable measurement; all analyses are conducted using the full (nondiscretized) scale, and plotting the average (nondiscretized) sharing intentions looks qualitatively similar. For the equivalent plot using mean sharing intentions instead of the discretized percentages, see Figure S1 in the Supplemental Material available online. Error bars indicate 95% confidence intervals.

Fig. 3.

Relationship between the effect of the treatment in Study 2 and the average accuracy rating from participants in the accuracy condition of Study 1 as a function of headline veracity (true vs. false). The dashed line shows the best-fitting regression.