Zhao Y, Zhu S, Wan Q, et al. Understanding how and by whom COVID-19 misinformation is spread on social media: Coding and network analyses[J]. Journal of medical Internet research, 2022, 24(6): e37623.

Abstract：

Background:

During 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.

Objective:

We propose an elaboration likelihood model–based theoretical model to understand the persuasion process of COVID-19–related misinformation on social media.

Methods:

The 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.

Results:

Our 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.

Conclusions:

Our proposed model and findings could help to combat the spread of misinformation by detecting suspicious users and identifying propagation characteristics.

Keywords：health misinformation; COVID-19; social media; misinformation spread; infodemiology; global health crisis; misinformation;theoretical model; medical information; epidemic; pandemic

Foundation：National Natural Science Foundation of China (72004091, 72174083) and the Humanity and Social Science Foundation of Ministry of Education of China (20YJC870014)