Work

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

Year: 2024

Type: article

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.).

Source: Philosophical Transactions of the Royal Society B Biological Sciences

DOI: 10.1098/rstb.2023.0040