Understanding How and by Whom COVID-19 Misinformation is Spread on Social Media: Coding and Network Analyses

• Main Authors: Yuehua Zhao (Author), Sicheng Zhu (Author), Qiang Wan (Author), Tianyi Li (Author), Chun Zou (Author), Hao Wang (Author), Sanhong Deng (Author)
• Format: Book
• Published: JMIR Publications, 2022-06-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 1438-8871; 10.2196/37623
• Keywords: Computer applications to medicine. Medical informatics; R858-859.7; Public aspects of medicine; RA1-1270
• Source: Journal of Medical Internet Research, Vol 24, Iss 6, p e37623 (2022)

BackgroundDuring global health crises such as the COVID-19 pandemic, rapid spread of misinformation on social media has occurred. The misinformation associated with COVID-19 has been analyzed, but little attention has been paid to developing a comprehensive analytical framework to study its spread on social media. ObjectiveWe propose an elaboration likelihood model-based theoretical model to understand the persuasion process of COVID-19-related misinformation on social media. MethodsThe proposed model incorporates the central route feature (content feature) and peripheral features (including creator authority, social proof, and emotion). The central-level COVID-19-related misinformation feature includes five topics: medical information, social issues and people's livelihoods, government response, epidemic spread, and international issues. First, we created a data set of COVID-19 pandemic-related misinformation based on fact-checking sources and a data set of posts that contained this misinformation on real-world social media. Based on the collected posts, we analyzed the dissemination patterns. ResultsOur data set included 11,450 misinformation posts, with medical misinformation as the largest category (n=5359, 46.80%). Moreover, the results suggest that both the least (4660/11,301, 41.24%) and most (2320/11,301, 20.53%) active users are prone to sharing misinformation. Further, posts related to international topics that have the greatest chance of producing a profound and lasting impact on social media exhibited the highest distribution depth (maximum depth=14) and width (maximum width=2355). Additionally, 97.00% (2364/2437) of the spread was characterized by radiation dissemination. ConclusionsOur proposed model and findings could help to combat the spread of misinformation by detecting suspicious users and identifying propagation characteristics.