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

• DOI: 10.1017/ash.2024.379
• Publication Date: 2024-09-23T10:07:49Z
• Authors: Abdi D. Wakene; Lauren N. Cooper; John J. Hanna; Trish M. Perl; Christoph U. Lehmann; Richard J. Medford
• Keywords: Misinformation; Disinformation; Sentiment Analysis; Pandemic; False accusation

Abstract Objective: Social media’s arrival eased the sharing of mis- and disinformation. False information proved challenging throughout coronavirus disease 2019 (COVID-19) pandemic with many clinicians researchers analyzing “infodemic.” We systemically reviewed synthesized COVID-19 disinformation literature, identifying prevalence content false exploring mitigation prevention strategies. Design: identified analyzed publications on COVID-19-related published from March 1, 2020, to December 31, 2022, in PubMed. performed a manual topic review abstracts along automated modeling organize compare different themes. also conducted sentiment (ranked −3 +3) emotion analysis (rated as predominately happy, sad, angry, surprised, or fearful) abstracts. Results: 868 peer-reviewed scientific which 639 (74%) had available for automatic analysis. More than third described prevention-related issues. The mean score was 0.685, 56% studies negative (fear sadness most common emotions). Conclusions: Our comprehensive reveals significant proliferation dis- misinformation research during pandemic. study illustrates pivotal role social media amplifying information. Research into infodemic characterized by sentiments. Combining provided nuanced understanding complexities misinformation, highlighting themes such source effect strategies prevention.