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The Role of the Crowd in Countering Misinformation: A Case Study of the COVID-19 Infodemic
Open accessAbstract
Fact checking by professionals is viewed as a vital defense in the fight against misinformation. While fact checking is important and its impact has been significant, fact checks could have limited visibility and may not reach the intended audience, such as those deeply embedded in polarized communities. Concerned citizens (i.e., the crowd), who are users of the platforms where misinformation appears, can play a crucial role in disseminating fact-checking information and in countering the spread of misinformation. To explore if this is the case, we conduct a data-driven study of misinformation on the Twitter platform, focusing on tweets related to the COVID-19 pandemic, analyzing the spread of misinformation, professional fact checks, and the crowds response to popular misleading claims about COVID-19.In this work, we curate a dataset of false claims and statements that seek to challenge or refute them. We train a classifier to create a novel dataset of 155,468 COVID-19-related tweets, containing 33,237 false claims and 33,413 refuting arguments. Our findings show that professional fact-checking tweets have limited volume and reach. In contrast, we observe that the surge in misinformation tweets results in a quick response and a corresponding increase in tweets that refute such misinformation. More importantly, we find contrasting differences in the way the crowd refutes tweets, some tweets appear to be opinions, while others contain concrete evidence, such as a link to a reputed source. Our work provides insights into how misinformation is organically countered in social platforms by some of their users and the role they play in amplifying professional fact checks. These insights could lead to development of tools and mechanisms that can empower concerned citizens in combating misinformation. The code and data can be found in this link.1
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“as disinformation actors change their tactics (Kumar and Shah 2018; Micallef et al. 2020).”
From Fake news, disinformation and misinformation in social media: a review (2023) · cites this paper as Micallef et al. 2020
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Observed Correction Increases Accuracy
Bode, L., & Vraga, E. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780197565896.003.0003
Bode, L., & Vraga, E. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780197565896.003.0005
Bode, L., & Vraga, E. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780197565896.002.0006
Bode, L., & Vraga, E. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780197565896.003.0009
Bode, L., & Vraga, E. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780197565896.003.0008
Bode, L., & Vraga, E. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780197565896.003.0007
Metadata
- Authors
- Micallef, N., He, B., Kumar, S., Ahamad, M., & Memon, N.
- Affiliations
- New York University Abu Dhabi (AE)
- Georgia Institute of Technology (US)
- New York University (US)
- Venue
- 2020 IEEE International Conference on Big Data (Big Data)
- Publisher
- IEEE
- Published
- 2020-12-10
- Pages
- 748-757
- OpenAlex
- W3100943362
- Cited by
- 116
- References
- 57
- Citations by year
- 2026: 8 · 2025: 40 · 2024: 21 · 2023: 23 · 2022: 16 · 2021: 8
- FWCI
- 24.92
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE International Conference on Big Data (BigData) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (2027689)
- New York University Abu Dhabi
- York University
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Communication › Communication and COVID-19 Impact
- Keywords
- misinformation, COVID-19 infodemic, fact-checking, Twitter, false claims, misinformation spread
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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