The Misleading count: An identity-based intervention to counter partisan misinformation sharing

Abstract

Interventions to counter misinformation are often less effective for polarizing content on social media platforms. We sought to overcome this limitation by testing an identity-based intervention, which aims to promote accuracy by incorporating normative cues directly into the social media user interface. Across three pre-registered experiments in the U.S. (N=1,709) and UK (N=804), we found that crowdsourcing accuracy judgments by adding a Misleading count (next to the Like count) reduced participants’ reported likelihood to share inaccurate information about partisan issues by 25% (compared to a control condition). The Misleading count was also more effective when it reflected in-group norms (from fellow Democrats/Republicans) compared to the norms of general users, though this effect was absent in a less politically polarized context (UK). Moreover, the normative intervention was roughly 5 times as effective as another popular misinformation intervention (i.e., the accuracy nudge reduced sharing misinformation by 5%). Extreme partisanship did not undermine the effectiveness of the intervention. Our results suggest that identity-based interventions based on the science of social norms can be more effective than identity-neutral alternatives to counter partisan misinformation in politically polarized contexts (e.g., the U.S.).