Document Type

Article

Publication Date

7-31-2025

Department

College of Business

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.

Publisher's Statement

© The Author(s) 2025. Publisher’s version of record: https://doi.org/10.1007/s10796-025-10626-x

Publication Title

Information Systems Frontiers

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Version

Publisher's PDF

Download

Included in

Business Commons

Share

COinS