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Pausing to consider why a headline is true or false can help reduce the sharing of false news
Open accessAbstract
In an online experiment, participants who paused to explain why a headline was true or false indicated that they were less likely to share false information compared to control participants. Their intention to share accurate news stories was unchanged. These results indicate that adding “friction” (i.e., pausing to think) before sharing can improve the quality of information shared on social media.
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“In addition, because real (unaltered or authentic) materials are used and re-contextualized in a deceptive manner, they are difficult to detect and flag as disinformation (e.g., Fazio, 2020).”
From How persuasive are political cheapfakes disseminated via social media? The effects of out-of-context visual disinformation on message credibility and issue agreement (2025) · cites this paper as Fazio, 2020
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Metadata
- Authors
- Fazio, L.
- Affiliations
- Vanderbilt University (US)
- Corresponding author
- Fazio, Lisa
- ORCID
- Fazio, Lisa
- Venue
- Harvard Kennedy School Misinformation Review
- Published
- 2020-02-10
- ISSN
- 2766-1652 (ISSN-L)
- OpenAlex
- W3005166752
- Cited by
- 187
- References
- 12
- Citations by year
- 2026: 14 · 2025: 38 · 2024: 24 · 2023: 38 · 2022: 32 · 2021: 19 · 2020: 20
- FWCI
- 36.79
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Harvard Kennedy School Misinformation Review (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Library and Information Sciences › Academic Publishing and Open Access
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- misinformation, social media, information quality
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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