The DOI matches its Crossref record; 53 of the 57 references it cites were found in a registry or an index.
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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Cites
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Background
Flagged first, then by citations
4 of the 57 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- 2020, Novel coronavirus (2019-nCoV)
- pennebaker, 2001, Linguistic inquiry and word count: Liwc 2001, Mahway Lawrence Erlbaum Associates, 71, 2001
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Items 101–116 of 116
Bode, L., & Vraga, E. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780197565896.003.0001
Bode, L., & Vraga, E. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780197565896.002.0009
Bode, L., & Vraga, E. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780197565896.003.0004
The Theory of Observed Correction
Bode, L., & Vraga, E. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780197565896.003.0002
Bode, L., & Vraga, E. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780197565896.003.0006
Bode, L., & Vraga, E. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780197565896.003.0010
Bode, L., & Vraga, E. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780197565896.002.0007
Bode, L., & Vraga, E. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780197565896.002.0004
Frisli, S. (2025) · Social Media + Society · doi:10.1177/20563051251347614 · Cited by 1
Lin, L., Feng, Z., Wang, L., & Wong, K.-F. (2025) · Communications in computer and information science · doi:10.1007/978-981-95-4091-4_16
Employing Transformers and Humans for Textual-Claim Verification
Saeed, M. (2022) · theses.fr (ABES) · doi:10.70675/3e02a3a6z8281z4d84z8f38ze617eaaf969b
The Role of Presentation Styles in Countering Misinformation on Short Video Platforms CSCW039
Gunasekara, S., Sew, C., Pareek, S., Kelly, R., Kostakos, V., & Gonçalves, J. (2026) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3788075
Tang, R., Bode, L., & Vraga, E. K. (2026) · Journal of Health Communication · doi:10.1080/10810730.2026.2675027
Noman, M., Alam, F., & Ali, R. (2026) · SN Computer Science · doi:10.1007/s42979-026-05260-6
Heikkilä, T. (2026) · Public Understanding of Science · doi:10.1177/09636625261470329 · Cited by 1
Liu, Y., & Yue, Z. (2026) · JMIR Infodemiology · doi:10.2196/79434
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
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