tetano
Editor, Senior Moderator
JMIR Infodemiology
. 2026 Oct 29:6:e79434.
doi: 10.2196/79434.
Yang Liu 1 , Zhiying Yue 2
Affiliations Expand
Background: During the COVID-19 pandemic, social media platforms became major channels for public health communication and public sensemaking, but they also enabled the rapid circulation of rumors, false claims, conspiracy narratives, and misleading health information. Research on online COVID-19 misinformation expanded rapidly during the peak pandemic period, yet the evidence base was fragmented across public health, computing, communication, and information science.
Objective: This scoping review synthesizes empirical research on online COVID-19 misinformation on social media during the peak pandemic period from February 2020 to October 2022. It examines how misinformation was conceptualized, which platforms and public health contexts were emphasized, and how computational methods shaped the evidence base for infodemic management. Its distinct contribution is an integrated comparison of conceptual definitions, thematic and platform concentration, the computational research pipeline, and the gap between detection and actionable infodemic governance.
Methods: We conducted a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)-informed scoping review of empirical studies indexed in PubMed and IEEE Xplore in November 2022 using COVID-19, social media platforms, and misinformation-related terms. Eligible reports were English-language, peer-reviewed empirical studies published between February 2020 and October 2022 that analyzed social media data using computational, quantitative, or social media analytic methods; the language restriction applied to the publication, not to the underlying dataset. The final synthesis included 100 studies selected from 1688 records.
Results: Within the included corpus, the literature was concentrated around vaccine-related discourse, public health and social measures-related misinformation, and text-based data from X (X Corp), formerly known as Twitter. Studies commonly used misinformation detection, classification, sentiment analysis, topic modeling, and network analysis. Fewer studies examined diffusion pathways, source tracing, cross-platform movement, intervention effectiveness, or platform governance. The evidence base remains more developed for detecting and describing misinformation than for evaluating actionable responses to it.
Conclusions: Peak-pandemic COVID-19 misinformation research produced important computational and empirical knowledge, but it remains detection-centered and unevenly connected to public health action. Future infodemiology research should link computational outputs to intervention design, risk communication, fact-checking workflows, platform moderation, multilingual and multimodal analysis, and the evaluation of infodemic governance strategies.
Keywords: COVID-19; infodemic management; infodemiology; machine learning; misinformation; natural language processing; public health communication; scoping review; social media.
. 2026 Oct 29:6:e79434.
doi: 10.2196/79434.
Online COVID-19 Misinformation and Infodemic Governance During the Peak Pandemic Period: Scoping Review of Social Media Research From 2020 to 2022
Yang Liu 1 , Zhiying Yue 2
Affiliations Expand
- PMID: 42956193
- PMCID: PMC13718887
- DOI: 10.2196/79434
Abstract
Background: During the COVID-19 pandemic, social media platforms became major channels for public health communication and public sensemaking, but they also enabled the rapid circulation of rumors, false claims, conspiracy narratives, and misleading health information. Research on online COVID-19 misinformation expanded rapidly during the peak pandemic period, yet the evidence base was fragmented across public health, computing, communication, and information science.
Objective: This scoping review synthesizes empirical research on online COVID-19 misinformation on social media during the peak pandemic period from February 2020 to October 2022. It examines how misinformation was conceptualized, which platforms and public health contexts were emphasized, and how computational methods shaped the evidence base for infodemic management. Its distinct contribution is an integrated comparison of conceptual definitions, thematic and platform concentration, the computational research pipeline, and the gap between detection and actionable infodemic governance.
Methods: We conducted a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)-informed scoping review of empirical studies indexed in PubMed and IEEE Xplore in November 2022 using COVID-19, social media platforms, and misinformation-related terms. Eligible reports were English-language, peer-reviewed empirical studies published between February 2020 and October 2022 that analyzed social media data using computational, quantitative, or social media analytic methods; the language restriction applied to the publication, not to the underlying dataset. The final synthesis included 100 studies selected from 1688 records.
Results: Within the included corpus, the literature was concentrated around vaccine-related discourse, public health and social measures-related misinformation, and text-based data from X (X Corp), formerly known as Twitter. Studies commonly used misinformation detection, classification, sentiment analysis, topic modeling, and network analysis. Fewer studies examined diffusion pathways, source tracing, cross-platform movement, intervention effectiveness, or platform governance. The evidence base remains more developed for detecting and describing misinformation than for evaluating actionable responses to it.
Conclusions: Peak-pandemic COVID-19 misinformation research produced important computational and empirical knowledge, but it remains detection-centered and unevenly connected to public health action. Future infodemiology research should link computational outputs to intervention design, risk communication, fact-checking workflows, platform moderation, multilingual and multimodal analysis, and the evaluation of infodemic governance strategies.
Keywords: COVID-19; infodemic management; infodemiology; machine learning; misinformation; natural language processing; public health communication; scoping review; social media.