Social-media companies must flatten the curve of misinformation
Integrity signals
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Claims the paper makes
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
On social media platforms, deepfakes can be disseminated quickly and reach a large audience before they are detected and removed.
“This is particularly true on social media platforms, where deepfakes can be disseminated quickly and reach a large audience before they are detected and removed (Donovan, 2020).”
From Deepfake forensics: a survey of digital forensic methods for multimodal deepfake identification on social media (2024) · cites this paper as Donovan, 2020
Cited by
Sorted by citations
Zuboff, S. (2022) · Organization Theory · doi:10.1177/26317877221129290 · Cited by 209 · see citing works
“No matter how often and loudly the public and its lawmakers are stirred to outrage insisting that Facebook must take responsibility (Donovan, 2020; see also, Atlantic Council, 2021; Browning & Mac, 2022; Frenkel, 2021; Harwell & Oremus, 2022), the engineers say that the company simply cannot comply.”
BackgroundAI chatbots and (mis)information in public health: impact on vulnerable communities.
Meyrowitsch, D. W., Jensen, A. K., Sørensen, J. B., & Varga, T. V. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1226776 · Cited by 73 · see citing works
Qureshi, S. M., Saeed, A., Almotiri, S. H., Ahmad, F., & Al Ghamdi, M. A. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.2037 · Cited by 71 · see citing works
Sustained online amplification of COVID-19 elites in the United States
Gallagher, R. J., Doroshenko, L., Shugars, S., Lazer, D., & Foucault Welles, B. (2021) · Social Media + Society · doi:10.1177/20563051211024957 · Cited by 80 · see citing works
Safe from “harm”: The governance of violence by platforms
DeCook, J. R., Cotter, K., Kanthawala, S., & Foyle, K. (2022) · Policy & Internet · doi:10.1002/poi3.290 · Cited by 46 · see citing works
Characterizing AI-Generated Misinformation on Social Media
Drolsbach, C., Demirel, E., & Pröllochs, N. (2025) · arXiv · doi:10.48550/arxiv.2505.10266
“Further, they lack scalability, which makes them ineffective for large volumes of media (Groh et al. 2024).”
- Authors
- Donovan, J.
- Affiliations
- Harvard University (US)
- Corresponding author
- Donovan, Joan
- ORCID
- Donovan, Joan
- Venue
- Nature
- Published
- 2020-04-14
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W3016224303
- Cited by
- 110
- References
- 0
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 13 · 2023: 14 · 2022: 27 · 2021: 28 · 2020: 14 · 2018: 1
- FWCI
- 27.70
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- 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
- misinformation, social networking sites, COVID-19 pandemic, online fraud, hoaxes, political misinformation, content moderation, disinformation
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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.
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