The DOI matches its Crossref record.
An exploration of how fake news is taking over social media and putting public health at risk
Open accessAbstract
Recent statistics show that almost 1/4 of a million people have died and four million people are affected either with mild or serious health problems caused by coronavirus (COVID‐19). These numbers are rapidly increasing (World Health Organization, May 3, 2020c). There is much concern during this pandemic about the spread of misleading or inaccurate information. This article reports on a small study which attempted to identify the types and sources of COVID‐19 misinformation. The authors identified and analysed 1225 pieces of COVID‐19 fake news stories taken from fact‐checkers, myth‐busters and COVID‐19 dashboards. The study is significant given the concern raised by the WHO Director‐General that ‘we are not just fighting the pandemic, we are also fighting infodemic’. The study concludes that the COVID‐19 infodemic is full of false claims, half backed conspiracy theories and pseudoscientific therapies, regarding the diagnosis, treatment, prevention, origin and spread of the virus. Fake news is pervasive in social media, putting public health at risk. The scale of the crisis and ubiquity of the misleading information require that scientists, health information professionals and journalists exercise their professional responsibility to help the general public identify fake news stories. They should ensure that accurate information is published and disseminated.J.M.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Rocha, Y. M., de Moura, G. A., Desidério, G. A., de Oliveira, C. H., Lourenço, F. D., & de Figueiredo Nicolete, L. D. (2023) · Journal of Public Health · doi:10.1007/s10389-021-01658-z · Cited by 482
COVID-19 Misinformation Online and Health Literacy: A Brief Overview
Bin Naeem, S., & Kamel Boulos, M. N. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18158091 · Cited by 293
A deadly infodemic: social media and the power of COVID-19 misinformation
Gisondi, M. A., Barber, R., Faust, J. S., Raja, A., Strehlow, M. C., Westafer, L. M., & Gottlieb, M. (2022) · Journal of Medical Internet Research · doi:10.2196/35552 · Cited by 251
Gongane, V. U., Munot, M. V., & Anuse, A. D. (2022) · Social Network Analysis and Mining · doi:10.1007/s13278-022-00951-3 · Cited by 153
Evidence-based Fact-Checking of Health-related Claims
Sarrouti, M., Ben Abacha, A., Mrabet, Y., & Demner-Fushman, D. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021 · doi:10.18653/v1/2021.findings-emnlp.297 · Cited by 71
Metadata
- Authors
- Naeem, S. B., Bhatti, R., & Khan, A.
- Venue
- Health Information & Libraries Journal
- Publisher
- Wiley
- Published
- 2020-07-12
- Volume
- 38
- Issue
- 2
- Pages
- 143-149
- ISSN
- 1471-1834 (ISSN-L)
- 1471-1842
- OpenAlex
- W3041311242
- Cited by
- 488
- References
- 0
- FWCI
- 102.46
- Type
- journal article
- Language
- en
- Open access
- green
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 ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: An exploration of how fake news is taking over social media and putting public health at risk. https://app.cochat.ai/papers/10.1111/hir.12320
- The paper
- Naeem, S. B., Bhatti, R., & Khan, A. (2021). An exploration of how fake news is taking over social media and putting public health at risk. Health Information & Libraries Journal. https://doi.org/10.1111/hir.12320
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.