The COVID-19 infodemic
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The diffusion of misinformation through rapidly disseminated channels such as mass media and social networks can generate the phenomenon known as infodemic.
“The diffusion of misinformation conveyed through rapidly disseminated channels such as mass media and social networks, can generate the phenomenon known as infodemic (57).”
From ChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health (2023) · cites this paper as [57]
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Sachs, J. D., Karim, S. S. A., Aknin, L., Allen, J., Brosbøl, K., Colombo, F., Barron, G. C., Espinosa, M. F., Gaspar, V., Gaviria, A., et al. (2022) · The Lancet · doi:10.1016/s0140-6736(22)01585-9 · Cited by 842 · see citing works
ChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health
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“As an individual's information processing is sensitive to the affect associated with uncertainty appraisals, it is necessary to distinguish affect at a more fine-grained level than merely positive and negative (Tiedens & Linton, 2001).”
BackgroundMining Dual Emotion for Fake News Detection
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“The most recent example is the concomitant infodemic during the COVID-19 pandemic across the world [41].”
BackgroundSentiment Analysis for Fake News Detection
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“In the case of COVID-19, uncertainty and fear over both the virus, vaccines, and government restrictions, as well as a lack of consistent public communication, fuelled an ‘ infodemic ’ that threatened to undermine broad support for public health interventions (Lancet Editorial, 2020).”
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“We further controlled for political affiliation, age, gender, and race given the early evidence that COVID-19 beliefs, knowledge, and responses have been stratified by these demographic variables (Alsan et al., 2020; Jurkowitz and Mitchell, 2020; Tyson, 2020).”
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“Not only is a large amount of information available, but a significant amount of it must be considered misinformation because the sources of the information must be classified questionable [13,14].”
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“In fact, the COVID-19 pandemic has also caused a parallel epidemic, dubbed an ‘infodemic’ by WHO, where groups with political interests spread deliberately misinformation to destabilize the democratic system and discredit ‘elite’ knowledge provided by science and medical research (Anonymous, 2020).”
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“Moreover, academics are likely to have somewhat different access to diverse resources, such as those which may perpetuate the ‘infodemic’, flooded with unsubstantiated conspiracy theory materials [16].”
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“Tragically, this may increase the chances of people joining the infodemic, with its misleading and frequently fabricated news, so beloved of conspiracy theorists (Lancet, 2020).”
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“This finding also highlights the necessity of clear communication strategies from health and political authorities, especially under pandemic conditions that risks being dominated by confusing or misleading information leading people to engage in risky behavior that can compromise their own others’ health [25].”
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COVID-19 Vaccine Misperceptions in a Community Sample of Adults Aged 18–49 Years in Australia
Pickles, K. M., Copp, T., Meyerowitz‐Katz, G., Dodd, R. H., Bonner, C., Nickel, B., Steffens, M., Seale, H., Cvejic, E., Taba, M., et al. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19116883 · Cited by 27 · see citing works
Zheng, X., Zhang, J., Ye, X., Lin, X., Liu, H., Qin, Z., Chen, D., & Zhan, C. (2022) · Journal of Nursing Management · doi:10.1111/jonm.13846 · Cited by 19 · see citing works
MacKay, M., Ford, C., Colangeli, T., Gillis, D. J., McWhirter, J. E., & Papadopoulos, A. (2022) · BMC Public Health · doi:10.1186/s12889-022-13129-5 · Cited by 24 · see citing works
“Issues with distrust among the public [9, 10], criticisms regarding lack of transparency [11], and inconsistent and unclear communications [12, 13], as well as the current infodemic [14, 15] have undermined the efforts of official actors.”
BackgroundLotto, M., Hanjahanja-Phiri, T., Padalko, H., Oetomo, A., Butt, Z. A., Boger, J. N., Millar, J., Cruvinel, T., & Morita, P. P. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1130079 · Cited by 23 · see citing works
Social Media Big Data: The Good, The Bad, and the Ugly (Un)truths
Chew, A., & Gunasekeran, D. V. (2021) · Frontiers in Big Data · doi:10.3389/fdata.2021.623794 · Cited by 25 · see citing works
“This imposed an additional strain on humanity’s coordinated attempts to eradicate COVID-19, hindering public health risk communication (The Lancet Infectious Diseases, 2020).”
BackgroundLi, H., Huang, J., Ji, M., Yang, Y., & An, R. (2025) · Journal of Medical Internet Research · doi:10.2196/66098 · Cited by 25 · see citing works
Kusama, T., Kiuchi, S., Takeuchi, K., Ikeda, T., Nakazawa, N., Kinugawa, A., Osaka, K., & Tabuchi, T. (2022) · Healthcare · doi:10.3390/healthcare10030521 · Cited by 22 · see citing works
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Wyszynski, D. F., Hernández–Dı́az, S., Gordon-Dseagu, V., Ramiro, N., Basu, A., Kim, H. H., & Koenen, K. C. (2021) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-021-04241-2 · Cited by 24 · see citing works
Ezzeddine, F., Ayoub, O., Giordano, S., Nogara, G., Sbeity, I., Ferrara, E., & Luceri, L. (2023) · EPJ Data Science · doi:10.1140/epjds/s13688-023-00423-4 · Cited by 22 · see citing works
Biodefence research two decades on: worth the investment?
Long, C. M., & Marzi, A. (2021) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(21)00382-0 · Cited by 20 · see citing works
- Authors
- Diseases, T. L. I.
- Corresponding author
- The Lancet Infectious Diseases
- Venue
- The Lancet Infectious Diseases
- Publisher
- Elsevier BV
- Published
- 2020-08
- Volume
- 20
- Issue
- 8
- Pages
- 875
- ISSN
- 1473-3099 (ISSN-L)
- PMC
- PMC7367666
- OpenAlex
- W4205924832
- Cited by
- 421
- References
- 0
- Citations by year
- 2026: 20 · 2025: 38 · 2024: 52 · 2023: 79 · 2022: 132 · 2021: 82 · 2020: 16 · 2018: 1 · 2013: 1
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The Lancet Infectious Diseases (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 › Decision Sciences › Information Systems and Management › Academic Publishing and Open Access
- Keywords
- COVID-19 pandemic, infodemic, misinformation, fake news, conspiracy theories, social media, trust in health institutions, infodemiology, World Health Organization
- MeSH terms
- COVID-19, Communication, Health Policy, Humans, Pneumonia, Viral, Publishing, Journalism, Medical, Coronavirus Infections, Information Dissemination, Pandemics, 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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