A deadly infodemic: social media and the power of COVID-19 misinformation
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Abstract
COVID-19 is currently the third leading cause of death in the United States, and unvaccinated people continue to die in high numbers. Vaccine hesitancy and vaccine refusal are fueled by COVID-19 misinformation and disinformation on social media platforms. This online COVID-19 infodemic has deadly consequences. In this editorial, the authors examine the roles that social media companies play in the COVID-19 infodemic and their obligations to end it. They describe how fake news about the virus developed on social media and acknowledge the initially muted response by the scientific community to counteract misinformation. The authors then challenge social media companies to better mitigate the COVID-19 infodemic, describing legal and ethical imperatives to do so. They close with recommendations for better partnerships with community influencers and implementation scientists, and they provide the next steps for all readers to consider. This guest editorial accompanies the Journal of Medical Internet Research special theme issue, “Social Media, Ethics, and COVID-19 Misinformation.”
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Various political leaders publicly undermined WHO, campaigned against it and its advice, and tried to halt its funding.
“various political leaders publicly undermined WHO, disseminated campaigns against it and its advice, and even tried to halt its funding.”
From The Lancet Commission on lessons for the future from the COVID-19 pandemic (2022) · cites this paper as [234]
New forms of disinformation through digital media are especially powerful in disseminating ideas because of their reach, immediacy, relative anonymity, and the ease of creating echo chambers.
“Now we are experiencing new forms of disinformation through digital media,231 which are especially powerful in disseminating ideas because of their reach (eg, Facebook has nearly 2·4 billion subscribers), immediacy, relative anonymity, and the ease of creating echo chambers of tightly clustered groups receiving utterly different impressions of the facts from other online communities.”
From The Lancet Commission on lessons for the future from the COVID-19 pandemic (2022) · cites this paper as [234]
Social media and gray literature have already been a ground for infodemic.
“Social media and gray literature have already been the ground for infodemic, (63)”
From ChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health (2023) · cites this paper as [63]
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- Authors
- Gisondi, M. A., Barber, R., Faust, J. S., Raja, A., Strehlow, M. C., Westafer, L. M., & Gottlieb, M.
- Affiliations
- Stanford Medicine (US)
- Stanford University (US)
- Harvard University (US)
- University of Massachusetts Chan Medical School (US)
- Rush University (US)
- Corresponding author
- Gisondi, Michael A
- ORCID
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2022-02-01
- Volume
- 24
- Issue
- 2
- Pages
- e35552
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC8812140
- OpenAlex
- W4205472032
- Cited by
- 251
- References
- 19
- Citations by year
- 2026: 25 · 2025: 49 · 2024: 61 · 2023: 81 · 2022: 34 · 2021: 1
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Communication and COVID-19 Impact
- Social Sciences › Health › Vaccine Coverage and Hesitancy
- Keywords
- COVID-19 misinformation, infodemic, social media, disinformation, vaccine hesitancy, vaccine refusal, fake news, misinformation mitigation, social media ethics, legal obligations, community influencers, implementation science, scientific community response
- MeSH terms
- COVID-19, SARS-CoV-2, Infodemic, Communication, Humans, United States, Social Media
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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