Anger expressions of social media users following different crisis response strategies towards health emergency

Abstract

Although studies have argued the relationship of anger expressions with the satisfaction of crisis response strategies in traditional studies, fewer studies had integrated large-scale user-generated social media data to characterize public's dynamic anger expressions in multiple crisis stages following different response strategies. Drawing on the Situational Crisis Communication Theory and text mining techniques, this study analyzed the anger expressions in Weibo posts using Linguistic Inquiry and Word Count (LIWC, 2015), and the statistical comparison results suggested that the number of anger words after the involved company conducted deny strategy was significantly larger than the number of anger words when diminish and rebuild strategies were applied. Furthermore, using Biterm Topic Model, we observed dynamic changes of anger-related posts' themes following different crisis response strategies. Our research serves as an impetus for authorities to systematically examine the manifestations of anger exhibited by individual users on social media platforms, thereby enabling an assessment of public sentiments pertaining to diverse strategies. This also empowers practitioners to enhance the precision and efficacy of crisis phase-specific responses within the domain of crisis communication.