ArticleNature communications2022
Measuring exposure to misinformation from political elites on Twitter.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 21 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
21 citing papers in PubMed.
- Community-based fact-checking reduces the spread of misleading posts on X (formerly Twitter).Nature communications · 2026Article
- Divergent patterns of engagement with partisan and low-quality news across seven social media platforms.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Republicans are flagged more often than Democrats for sharing misinformation on X's Community Notes.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- How media competition fuels the spread of misinformation.Science advances · 2025Article
- Designing social media content recommendation algorithms for societal good.Annals of the New York Academy of Sciences · 2025Article
- Different honesty conceptions align across US politicians' tweets and public replies.Nature communications · 2025Article
- Differential impact from individual versus collective misinformation tagging on the diversity of Twitter (X) information engagement and mobility.Nature communications · 2025Article
- Fact-checks focus on famous politicians, not partisans.PNAS nexus · 2025Article
- Patterns of partisan toxicity and engagement reveal the common structure of online political communication across countries.Nature communications · 2024Article
- Continued influence of false accusations in forming impressions of political candidates.PNAS nexus · 2024Article
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- Proof of biased behavior of Normalized Mutual Information.Scientific reports · 2024Article
- Containing misinformation: Modeling spatial games of fake news.PNAS nexus · 2024Article
- Misinformation and harmful language are interconnected, rather than distinct, challenges.PNAS nexus · 2024Article
- Partisan styles of self-presentation in U.S. Twitter bios.Scientific reports · 2024Article
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- Partisan conflict over content moderation is more than disagreement about facts.Science advances · 2023Article
- Accuracy and social motivations shape judgements of (mis)information.Nature human behaviour · 2023Article
- Measuring exposure to misinformation from political elites on Twitter.Nature communications · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Misinformation can come directly from public figures and organizations (referred to here as "elites"). Here, we develop a tool for measuring Twitter users' exposure to misinformation from elites based on the public figures and organizations they choose to follow. Using a database of professional fact-checks by PolitiFact, we calculate falsity scores for 816 elites based on the veracity of their statements. We then assign users an elite misinformation-exposure score based on the falsity scores of the elites they follow on Twitter. Users' misinformation-exposure scores are negatively correlated with the quality of news they share themselves, and positively correlated with estimated conservative ideology. Additionally, we analyze the co-follower, co-share, and co-retweet networks of 5000 Twitter users and find an ideological asymmetry: estimated ideological extremity is associated with more misinformation exposure for users estimated to be conservative but not for users estimated to be liberal. Finally, we create an open-source R library and an Application Programming Interface (API) making our elite misinformation-exposure estimation tool openly available to the community.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.