Misunderstanding the harms of online misinformation
Abstract
The controversy over online misinformation and social media has opened a gap between public discourse and scientific research. Public intellectuals and journalists frequently make sweeping claims about the effects of exposure to false content online that are inconsistent with much of the current empirical evidence. Here we identify three common misperceptions: that average exposure to problematic content is high, that algorithms are largely responsible for this exposure and that social media is a primary cause of broader social problems such as polarization. In our review of behavioural science research on online misinformation, we document a pattern of low exposure to false and inflammatory content that is concentrated among a narrow fringe with strong motivations to seek out such information. In response, we recommend holding platforms accountable for facilitating exposure to false and extreme content in the tails of the distribution, where consumption is highest and the risk of real-world harm is greatest. We also call for increased platform transparency, including collaborations with outside researchers, to better evaluate the effects of online misinformation and the most effective responses to it. Taking these steps is especially important outside the USA and Western Europe, where research and data are scant and harms may be more severe.
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The study's sample of partisan U.S. users restricted to the web version of X during the months leading up to a U.S. presidential election may limit the generalizability of the findings beyond a period of heightened political activity.
“This context, marked by a high volume of political content and emotional engagement, enabled us to test interventions effectively, but may limit the generalizability of the findings (52, 53) beyond a period of heightened political activity.”
From Reranking partisan animosity in algorithmic social media feeds alters affective polarization (2025) · cites this paper as [52]
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- Authors
- Budak, C., Nyhan, B., Rothschild, D. M., Thorson, E., & Watts, D. J.
- Affiliations
- University of Michigan (US)
- Dartmouth College (US)
- Microsoft Research New York City (United States) (US)
- Syracuse University (US)
- University of Pennsylvania (US)
- Corresponding author
- Rothschild, David M.
- Venue
- Nature
- Published
- 2024-06-05
- Volume
- 630
- Issue
- 8015
- Pages
- 45-53
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W4399366359
- Cited by
- 156
- References
- 98
- Citations by year
- 2026: 65 · 2025: 71 · 2024: 20
- FWCI
- 16.23
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Social Media and Politics
- Social Sciences › Communication › Communication and COVID-19 Impact
- Keywords
- online misinformation, social media, algorithmic amplification, political polarization, platform accountability, platform transparency
- MeSH terms
- Disinformation, Algorithms, Communication, Humans, Motivation, Internet, 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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