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Cited by 116Misinformation and Its ImpactsComplex Network Analysis TechniquesCommunication and COVID-19 Impact

The Role of the Crowd in Countering Misinformation: A Case Study of the COVID-19 Infodemic

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Micallef, Nicholas, He, Bing, Kumar, Srijan, Ahamad, Mustaque, Memon, Nasir·2020·2020 IEEE International Conference on Big Data (Big Data)
DOI 10.1109/bigdata50022.2020.9377956
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Abstract

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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  • Actors who spread disinformation alter their methods.

    “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

    The Introduction describes characteristics of misinformation-spreading accounts, while Section VII examines tweet volume, engagement, and spread. Neither passage says that disinformation actors change their tactics; searches for changing tactics and evolving strategies found no such statement in our copy. (LLM, conf=0.72)

    Experimental — accuracy not yet measured

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4 of the 57 references couldn’t be matched to a known paper.

Research works printed but not linked: 3

  1. [17]17. Y. Wang, F. Ma, Z. Jin, Y. Yuan, G. Xun, K. Jha, L. Su, and J. Gao, "Eann: Event adversarial neural networks for multi-modal fake news detection," in Proceedings of the 24th acm sigkdd international conference on knowledge discovery & data mining, 2018, pp. 849-857.
  2. [31]31. M. Cinelli, W. Quattrociocchi, A. Galeazzi, C. M. Valensise, E. Brugnoli, A. L. Schmidt, P. Zola, F. Zollo, and A. Scala, "The covid-19 social media infodemic," arXiv preprint arXiv:2003.05004, 2020. · arXiv
  3. [53]53. J. W. Pennebaker, M. E. Francis, and R. J. Booth, "Linguistic inquiry and word count: Liwc 2001," Mahway: Lawrence Erlbaum Associates, vol. 71, no. 2001, p. 2001, 2001.

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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)
ORCID
Venue
2020 IEEE International Conference on Big Data (Big Data)
Publisher
IEEE
Published
2020-12-10
Pages
748-757
DOI
10.1109/bigdata50022.2020.9377956
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
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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