The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
COVID-19 Misinformation Online and Health Literacy: A Brief Overview
Open accessAbstract
Low digital health literacy affects large percentages of populations around the world and is a direct contributor to the spread of COVID-19-related online misinformation (together with bots). The ease and ‘viral’ nature of social media sharing further complicate the situation. This paper provides a quick overview of the magnitude of the problem of COVID-19 misinformation on social media, its devastating effects, and its intricate relation to digital health literacy. The main strategies, methods and services that can be used to detect and prevent the spread of COVID-19 misinformation, including machine learning-based approaches, health literacy guidelines, checklists, mythbusters and fact-checkers, are then briefly reviewed. Given the complexity of the COVID-19 infodemic, it is very unlikely that any of these approaches or tools will be fully effective alone in stopping the spread of COVID-19 misinformation. Instead, a mixed, synergistic approach, combining the best of these strategies, methods, and services together, is highly recommended in tackling online health misinformation, and mitigating its negative effects in COVID-19 and future pandemics. Furthermore, techniques and tools should ideally focus on evaluating both the message (information content) and the messenger (information author/source) and not just rely on assessing the latter as a quick and easy proxy for the trustworthiness and truthfulness of the former. Surveying and improving population digital health literacy levels are also essential for future infodemic preparedness.
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
Cites
1 retracted
RETRACTED: COVID-19 as an “Infodemic” in Public Health: Critical Role of the Social Media
Banerjee, D., & Meena, K. S. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.610623 · Cited by 161
RetractedLow Health Literacy and Health Outcomes: An Updated Systematic Review
Berkman, N. D., Sheridan, S. L., Donahue, K. E., Halpern, D. J., & Crotty, K. (2011) · Annals of Internal Medicine · doi:10.7326/0003-4819-155-2-201107190-00005 · Cited by 5,505
When Corrections Fail: The Persistence of Political Misperceptions
Nyhan, B., & Reifler, J. (2010) · Political Behavior · doi:10.1007/s11109-010-9112-2 · Cited by 3,173
Prevalence of health misinformation on social media: systematic review
Suarez-Lledo, V., & Alvarez-Galvez, J. (2021) · Journal of Medical Internet Research · doi:10.2196/17187 · Cited by 1,564
COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data
Ahmed, W., Vidal-Alaball, J., Downing, J., & López Seguí, F. (2020) · Journal of Medical Internet Research · doi:10.2196/19458 · Cited by 797
Health literacy and adherence to medical treatment in chronic and acute illness: A meta-analysis
Miller, T. A. (2016) · Patient Education and Counseling · doi:10.1016/j.pec.2016.01.020 · Cited by 662
Science vs Conspiracy: Collective Narratives in the Age of Misinformation
Bessi, A., Coletto, M., Davidescu, G. A., Scala, A., Caldarelli, G., & Quattrociocchi, W. (2015) · PLOS ONE · doi:10.1371/journal.pone.0118093 · Cited by 645
Leveraging media and health communication strategies to overcome the COVID-19 infodemic
Mheidly, N., & Fares, J. (2020) · Journal of Public Health Policy · doi:10.1057/s41271-020-00247-w · Cited by 522
An exploration of how fake news is taking over social media and putting public health at risk
Naeem, S. B., Bhatti, R., & Khan, A. (2021) · Health Information & Libraries Journal · doi:10.1111/hir.12320 · Cited by 488
Bridgman, A., Merkley, E., Loewen, P. J., Owen, T., Ruths, D., Teichmann, L., & Zhilin, O. (2020) · Harvard Kennedy School Misinformation Review · doi:10.37016/mr-2020-028 · Cited by 419
Cited by
Kožuh, I., & Čakš, P. (2023) · Healthcare · doi:10.3390/healthcare11202796 · Cited by 21
Faiola, A., Kamel Boulos, M. N., Bin Naeem, S., & ur-Rehman, A. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20010729 · Cited by 18
Self-citationBouclaous, C., Kamand, A. A., Daher, R., Alrazim, A., & Kaedbey, H. D. (2023) · Nordic Journal of Digital Literacy · doi:10.18261/njdl.18.1.6 · Cited by 17
Bin Naeem, S., Azam, M., Kamel Boulos, M. N., & Bhatti, R. (2024) · Information · doi:10.3390/info15040176 · Cited by 13
Metadata
- Authors
- Bin Naeem, S., & Kamel Boulos, M. N.
- Venue
- International Journal of Environmental Research and Public Health
- Publisher
- MDPI AG
- Published
- 2021-07-30
- Volume
- 18
- Issue
- 15
- Pages
- 8091
- ISSN
- 1660-4601 (ISSN-L)
- OpenAlex
- W3192149658
- Cited by
- 293
- References
- 39
- FWCI
- 73.61
- Type
- journal article
- Language
- en
- Open access
- gold
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: COVID-19 Misinformation Online and Health Literacy: A Brief Overview. https://app.cochat.ai/papers/10.3390/ijerph18158091
- The paper
- Bin Naeem, S., & Kamel Boulos, M. N. (2021). COVID-19 Misinformation Online and Health Literacy: A Brief Overview. International Journal of Environmental Research and Public Health. https://doi.org/10.3390/ijerph18158091
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.