What social media told us in the time of COVID-19: A scoping review
Open accessAbstract
With the onset of the COVID-19 pandemic, social media has rapidly become a crucial communication tool for information generation, dissemination, and consumption. In this scoping review, we selected and examined peer-reviewed empirical studies relating to COVID-19 and social media during the first outbreak from November, 2019, to November, 2020. From an analysis of 81 studies, we identified five overarching public health themes concerning the role of online social media platforms and COVID-19. These themes focused on: surveying public attitudes, identifying infodemics, assessing mental health, detecting or predicting COVID-19 cases, analysing government responses to the pandemic, and evaluating quality of health information in prevention education videos. Furthermore, our Review emphasises the paucity of studies on the application of machine learning on data from COVID-19-related social media and a scarcity of studies documenting real-time surveillance that was developed with data from social media on COVID-19. For COVID-19, social media can have a crucial role in disseminating health information and tackling infodemics and misinformation.
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The Global Quality Score (GQS) is a widely validated 5-point Likert scale originally developed for patient-facing web-based health content and subsequently validated for short-form video health content analyses.
“The Global Quality Score (GQS), a widely validated 5-point Likert scale (1 = poor to 5 = excellent) originally developed to evaluate the overall educational quality of patient-facing web-based health content and subsequently validated for use in short-form video health content analyses20–24”
From Quality, reliability, and treatment misinformation risk of epilepsy content on Chinese short-video platforms TikTok (Douyin) and Bilibili: a cross-sectional analysis (2026) · cites this paper as [20]
The Results table reports that one reviewed study used the Global Quality Score to assess COVID-19 YouTube videos. It does not describe the score as a five-point Likert scale, establish its origin in patient-facing web content, or report validation for short-form videos. (LLM, conf=0.84)
Experimental — accuracy not yet measuredOne-third of Covid-19 updates have been classified as fake.
“where one-third of Covid-19 updates have been classified as fake (20)”
From Digital Media Use and Adolescents' Mental Health During the Covid-19 Pandemic: A Systematic Review and Meta-Analysis (2022) · cites this paper as [20]
Social media platforms provided an overload of Covid-19 related information, adding additional stress to the already worrisome situation.
“However, social media platforms also provided an overload of Covid-19 related information, where one-third of Covid-19 updates have been classified as fake (20), thus adding additional stress to the already worrisome situation.”
From Digital Media Use and Adolescents' Mental Health During the Covid-19 Pandemic: A Systematic Review and Meta-Analysis (2022) · cites this paper as [20]
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Adebesin, F., Smuts, H., Mawela, T., Maramba, G., & Hattingh, M. (2023) · JMIR Infodemiology · doi:10.2196/48620 · Cited by 57 · see citing works
Changes to public health surveillance methods due to the COVID-19 pandemic: scoping review
Clark, E. C., Neumann, S., Hopkins, S., Kostopoulos, A., Hagerman, L., & Dobbins, M. (2024) · JMIR Public Health and Surveillance · doi:10.2196/49185 · Cited by 42 · see citing works
The impact of Facebook use on self-reported eating disorders during the COVID-19 lockdown
Mannino, G., Salerno, L., Bonfanti, R. C., Albano, G., & Lo Coco, G. (2021) · BMC Psychiatry · doi:10.1186/s12888-021-03628-x · Cited by 21 · see citing works
Zhang, Y.-J., Guo, Y.-Y., & Wang, Y. (2026) · Scientific Reports · doi:10.1038/s41598-026-52532-5 · Cited by 3 · see citing works
“…educational quality of patient-facing web-based health content and subsequently validated for use in short-form video health content analyses 20–24 , was employed to assess the holistic educational value of each video, including the completeness of presented clinical information, logical…”
- Authors
- Tsao, S.-F., Chen, H., Tisseverasinghe, T., Yang, Y., Li, L., & Butt, Z. A.
- Affiliations
- University of Waterloo (CA)
- Seneca Polytechnic (CA)
- Corresponding author
- Butt, Zahid A
- ORCID
- Tsao, Shu-Feng
- Venue
- The Lancet Digital Health
- Publisher
- Elsevier BV
- Published
- 2021-03
- Volume
- 3
- Issue
- 3
- Pages
- e175-e194
- ISSN
- 2589-7500 (ISSN-L)
- PMC
- PMC7906737
- OpenAlex
- W3120829418
- Cited by
- 816
- References
- 95
- Citations by year
- 2026: 44 · 2025: 110 · 2024: 147 · 2023: 207 · 2022: 211 · 2021: 97
- FWCI
- 36.71
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- The Lancet Digital Health (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- arXiv (Cornell University) (acceptedVersion)
- arXiv (Cornell University)
- Topics
- Social Sciences › Communication › Communication and COVID-19 Impact
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- COVID-19 pandemic, social media, infodemic, misinformation, public attitudes, mental health, case finding, government pandemic response, health information quality, machine learning, real-time surveillance, health communication
- MeSH terms
- COVID-19, SARS-CoV-2, Disease Outbreaks, Health Education, Humans, Public Health, 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.
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