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

Abdi D Wakene; Lauren N Cooper; John J Hanna; Trish M Perl; Christoph U Lehmann; Richard J Medford

doi:10.1017/ash.2024.379

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

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

Authors

Abdi D Wakene¹, Lauren N Cooper¹, John J Hanna^{1

2

3}, Trish M Perl², Christoph U Lehmann¹, Richard J Medford^{1

2

3

4}

Affiliations

¹ Clinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.
² Division of Infectious Diseases and Geographic Medicine, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA.
³ ECU Health, Greenville, NC, USA.
⁴ Brody School of Medicine, Department of Internal Medicine, East Carolina University, Greenville, NC, USA.

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.