The Sharing of Disaster-Related Information on Social Media

Abstract This paper explores how two types of textual features—uncertainty-related features and self-regulation-related features—affect information diffusion amid disasters. We identify four textual expressions (i.e., insight, netspeak, work, and reward) of social media posts that generate negative impacts on the diffusion of information. Against the backdrop of the COVID-19 pandemic, we conducted an econometrical study of COVID-19-related posts collected from Weibo, followed by an experiment, to examine the proposed relationships between the reposting behavior and the four textual expressions. Theoretically, we examined the potential effects of information avoidance on the sharing of information on social media during disasters. This study can improve the predictive performance in future disaster-related studies of social media. Practically, government officers are advised that these features may generate negative impacts on the reposting behavior of those who read their posts and hinder the transfer of official information and policy announcements.