Developing an accuracy-prompt toolkit to reduce COVID-19 misinformation online
Open accessAbstract
Recent research suggests that shifting users’ attention to accuracy increases the quality of news they subsequently share online. Here we help develop this initial observation into a suite of deploy-able interventions for practitioners. We ask (i) how prior results generalize to other approaches for prompting users to consider accuracy, and (ii) for whom these prompts are more versus less effec-tive. In a large survey experiment examining participants’ intentions to share true and false head-lines about COVID-19, we identify a variety of different accuracy prompts that su¬ccessfully increase sharing
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A variety of successful accuracy prompts have been identified.
“a variety of successful accuracy prompts have been identified25”
From Accuracy prompts are a replicable and generalizable approach for reducing the spread of misinformation (2022) · cites this paper as [25]
Prompting participants to think about accuracy reduces the disconnect between accuracy judgments and sharing intentions and thereby increases the quality of news shared.
“Evidence for the role of inattention comes from experiments in which prompting participants to think about the concept of accuracy – for example, by asking them to evaluate the accuracy of a random headline at the beginning of the study – reduces the disconnect between accuracy judgments and sharing intentions, and thereby increases the quality of news shared24–26.”
From Accuracy prompts are a replicable and generalizable approach for reducing the spread of misinformation (2022) · cites this paper as [25]
There is a disconnect between perceptions of accuracy and sharing intentions: even when participants are good at distinguishing true and false headlines when asked to judge accuracy, this ability often does not translate to sharing.
“Recent work has observed that there is a disconnect between perceptions of accuracy and sharing intentions: Even when participants are quite good at distinguishing between true and false headlines (if they are asked to judge accuracy), this ability to discern truth from falsehood often does not translate to sharing24–26.”
From Accuracy prompts are a replicable and generalizable approach for reducing the spread of misinformation (2022) · cites this paper as [25]
There is a dissociation between accuracy judgments and sharing intentions observed in past work.
“given the dissociation between accuracy judgments and sharing intentions observed in past work24–26”
From Accuracy prompts are a replicable and generalizable approach for reducing the spread of misinformation (2022) · cites this paper as [25]
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Ahmed, M. S., Pathak, R., & Spezzano, F. (2026) · ACM Transactions on Management Information Systems · doi:10.1145/3812650
- Authors
- Epstein, Z., Berinsky, A. J., Cole, R., Gully, A., Pennycook, G., & Rand, D. G.
- Affiliations
- MIT Media Lab (US)
- Massachusetts Institute of Technology (US)
- Google (United States) (US)
- University of Regina (CA)
- Venue
- Harvard Kennedy School Misinformation Review
- Published
- 2021-05-18
- ISSN
- 2766-1652 (ISSN-L)
- OpenAlex
- W3161219713
- Cited by
- 93
- References
- 13
- Citations by year
- 2026: 4 · 2025: 11 · 2024: 28 · 2023: 20 · 2022: 23 · 2021: 7
- FWCI
- 27.37
- 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)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by)
- OSF Preprints (OSF Preprints) (submittedVersion)
- PsyArXiv (OSF Preprints) (submittedVersion)
- PsyArXiv (OSF Preprints) (submittedVersion, public-domain)
- Funders
- Miami Foundation
- William and Flora Hewlett Foundation
- John Templeton Foundation
- Canadian Institutes of Health Research
- Social Sciences and Humanities Research Council of Canada
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Communication and COVID-19 Impact
- Social Sciences › Decision Sciences › Information Systems and Management › Academic Publishing and Open Access
- Keywords
- COVID-19 misinformation, misinformation interventions, survey experiment
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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