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. 2024 Sep 23;4(1):e141.
doi: 10.1017/ash.2024.379. eCollection 2024.

A pandemic of COVID-19 mis- and disinformation: manual and automatic topic analysis of the literature

Abdi D Wakene  1 , Lauren N Cooper  1 , John J Hanna  1   2   3 , Trish M Perl  2 , Christoph U Lehmann  1 , Richard J Medford  1   2   3   4
Affiliations

A pandemic of COVID-19 mis- and disinformation: manual and automatic topic analysis of the literature

Abdi D Wakene et al. Antimicrob Steward Healthc Epidemiol. .

Abstract

Objective: Social media's arrival eased the sharing of mis- and disinformation. False information proved challenging throughout the coronavirus disease 2019 (COVID-19) pandemic with many clinicians and researchers analyzing the "infodemic." We systemically reviewed and synthesized COVID-19 mis- and disinformation literature, identifying the prevalence and content of false information and exploring mitigation and prevention strategies.

Design: We identified and analyzed publications on COVID-19-related mis- and disinformation published from March 1, 2020, to December 31, 2022, in PubMed. We performed a manual topic review of the abstracts along with automated topic modeling to organize and compare the different themes. We also conducted sentiment (ranked -3 to +3) and emotion analysis (rated as predominately happy, sad, angry, surprised, or fearful) of the abstracts.

Results: We reviewed 868 peer-reviewed scientific publications of which 639 (74%) had abstracts available for automatic topic modeling and sentiment analysis. More than a third of publications described mitigation and prevention-related issues. The mean sentiment score for the publications was 0.685, and 56% of studies had a negative sentiment (fear and sadness as the most common emotions).

Conclusions: Our comprehensive analysis reveals a significant proliferation of dis- and misinformation research during the COVID-19 pandemic. Our study illustrates the pivotal role of social media in amplifying false information. Research into the infodemic was characterized by negative sentiments. Combining manual and automated topic modeling provided a nuanced understanding of the complexities of COVID-19-related misinformation, highlighting themes such as the source and effect of misinformation, and strategies for mitigation and prevention.

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Conflict of interest statement

None.

Figures

Figure 1.
Figure 1.
Search terms used for PubMed query.
Figure 2.
Figure 2.
CONSORT diagram detailing the literature review process.
Figure 3.
Figure 3.
Publication counts per topic each year between 2020 and 2022.
Figure 4.
Figure 4.
Sentiment analysis of 639 publication abstracts between 2020 and 2022.
Figure 5.
Figure 5.
Emotion analysis of publication abstracts.
See this image and copyright information in PMC

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