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

Clara Pretus, Ali M. Javeed, Diána Hughes, Kobi Hackenburg, Manos Tsakiris, Oscar Vilarroya, Jay J. Van Bavel

Research output: Contribution to journalArticlepeer-review


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 US (N = 1709) and UK (N = 804), we found that crowdsourcing accuracy judgements by adding a Misleading count (next to the Like count) reduced participants' reported likelihood to share inaccurate information about partisan issues by 25% (compared with a control condition). The Misleading count was also more effective when it reflected in-group norms (from fellow Democrats/Republicans) compared with the norms of general users, though this effect was absent in a less politically polarized context (UK). Moreover, the normative intervention was roughly five 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 US).

Original languageEnglish (US)
Article number20230040
JournalPhilosophical Transactions of the Royal Society B: Biological Sciences
Issue number1897
StatePublished - Mar 11 2024


  • intervention
  • misinformation
  • social identity
  • social media
  • social norms

ASJC Scopus subject areas

  • General Biochemistry, Genetics and Molecular Biology
  • General Agricultural and Biological Sciences


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