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How to fight an infodemic
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“Under these current plausible COVID-19 vaccine acceptance rates, possible levels of existing protective immunity-though it is unclear whether post-infection immunity confers long-term immunity 29 and the rapidly evolving nature of misinformation surrounding the pandemic 23,30 , it is unclear whether vaccination will reach the levels required for herd immunity.”
From Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA (2021) · cites this paper as [30]
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“In addition to the physical health impacts of COVID-19, there is a rapid amplification of both accurate and inaccurate information on digital platforms worldwide [3].”
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In the citing paper
“While accessible, non-visual techniques to present a variety of data graphics have largely been investigated in research [43,92,93,130,134], few have been translated to standard web tools for use in practice.”
From The Accessibility of Data Visualizations on the Web for Screen Reader Users: Practices and Experiences During COVID-19 (2023) · cites this paper as [130]
Experimental — accuracy not yet measuredIn the citing paper
“Public support for policies to control the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is being undercut by misinformation, leading to the World Health Organization's 'infodemic' declaration (6).”
From Misinformation in and about science (2021) · cites this paper as [6]
In the citing paper
“Infodemic can influence medical decision-making on treatment or preventive measures, (59, 60) for example some people used hydroxychloroquine as a treatment for COVID-19 based on false or unproven information, endorsed by popular and influential people (61).”
From ChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health (2023) · cites this paper as [59]
In the citing paper
“In this study, we show that information dynamics tailored to alter individuals' perceptions and, consequently, their behavioral response, is able to drive collective attention 12 towards false 13,14 or inflammatory 15 content, a phenomenon named infodemics [16][17][18][19] , sharing similarities with more traditional epidemics and spreading phenomena [20][21][22] .”
From Assessing the risks of ‘infodemics’ in response to COVID-19 epidemics (2020) · cites this paper as [12]
In the citing paper
“Lacking a developed framework, tech companies have fumbled their way through the ongoing coronavirus pandemic, unable to stem the “infodemic” of misinformation”
From Stewardship of global collective behavior (2021) · cites this paper as [18]
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Lewandowsky, S., Ecker, U. K. H., Cook, J., van der Linden, S., Roozenbeek, J., & Орескес, Н. (2023) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2023.101711 · Cited by 123
Chisita, C. T. (2020) · Library Hi Tech News · doi:10.1108/lhtn-03-2020-0022 · Cited by 71
Metadata
- Authors
- Zarocostas, J.
- Corresponding author
- Zarocostas, John
- Venue
- The Lancet
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 395
- Issue
- 10225
- Pages
- 676
- ISSN
- 0099-5355 (ISSN-L)
- 0140-6736
- PMC
- PMC7133615
- OpenAlex
- W3007198457
- Cited by
- 2,344
- References
- 0
- Citations by year
- 2026: 119 · 2025: 187 · 2024: 253 · 2023: 385 · 2022: 499 · 2021: 564 · 2020: 337
- FWCI
- 597.27
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The Lancet (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Communication and COVID-19 Impact
- Social Sciences › Communication › Public Relations and Crisis Communication
- Keywords
- infodemic, COVID-19 misinformation, risk communication, social media, public health communication, rumors, health emergency preparedness, public trust in health information, public health emergency of international concern
- MeSH terms
- COVID-19, Humans, Pneumonia, Viral, Global Health, World Health Organization, Coronavirus Infections, Pandemics
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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