Measuring exposure to misinformation from political elites on Twitter
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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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The mean partisanship score among authors of AI-generated posts is significantly higher than among authors of non-AI-generated posts (0.274 vs 0.192, t = -4.16, p < 0.001).
“We observe a significantly higher mean partisanship score among authors of AI-generated posts compared to those of nonAI-generated posts (see Fig. 7a), indicating that authors of AI-generated content tend to be more conservative. The partisanship score ranges from -1 (Democrat) to 1 (Republican), with higher values indicating greater conservatism (Mosleh and Rand 2022). Specifically, the mean part”
From Characterizing AI-Generated Misinformation on Social Media (2025) · cites this paper as Mosleh and Rand 2022
The citation is attached to the explanation of the partisanship score, not to the citing authors’ AI-generated-post comparison. The source’s Methods section (‘Estimating users’ political ideology’; page unavailable) supports the direction of the score—higher means more conservative—but gives a range of −2.5 to 2.5, not −1 to 1. It does not report the citing authors’ comparison of AI-generated and non-AI-generated posts. (LLM, conf=0.88)
Experimental — accuracy not yet measuredIf misinformation is defined as misleading claims made by politicians and public figures or political images, then misinformation exposure is likely much more common and potentially impactful.
“However, if misinformation is defined as misleading claims made by politicians and public figures (Mosleh & Rand 2022) or political images (Yang et al. 2023), then misinformation exposure is likely much more common and potentially impactful.”
From Social Media and Morality. (2024) · cites this paper as Mosleh & Rand 2022
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- Authors
- Mosleh, M., & Rand, D. G.
- Affiliations
- University of Exeter (GB)
- Massachusetts Institute of Technology (US)
- Corresponding author
- Mosleh, Mohsen
- Venue
- Nature Communications
- Published
- 2022-11-21
- Volume
- 13
- Issue
- 1
- Pages
- 7144-7144
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC9681735
- OpenAlex
- W4309650732
- Cited by
- 90
- References
- 39
- Citations by year
- 2026: 18 · 2025: 25 · 2024: 29 · 2023: 14 · 2022: 3 · 2021: 1
- FWCI
- 25.54
- Type
- journal article
- Language
- en
- Update notices
- correction (2022-12-13)
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (2047152)
- Alfred P. Sloan Foundation (2021-16891)
- Amazon Web Services
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Social Media and Politics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- misinformation exposure, political elites, Twitter, social media followers, fact-checking, political ideology, ideological asymmetry, news quality, conservatism
- MeSH terms
- Communication, Extremities, Humans, Gene Library, Databases, Factual, Social Media
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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