Public emotional atmosphere during disasters: understanding emotions in short video comments on the Zhengzhou flood

Abstract

Short videos are gaining popularity among Chinese netizens who use them to socialize and express themselves online, particularly during disasters. This study examines the characteristics of an emotional atmosphere through short video comments. We developed the Multimodal Social Short Video Crawler V1.0 to analyze 157,747 comments from 343 short videos about the “Zhengzhou flood.” First, we categorized comments into clusters and calculated cluster density using complex network analysis methods. Second, we employed computer-mediated content analysis to derive sentiment values in clusters. To capture the emotional atmosphere in short videos accurately, we developed a trust model to assign weights to both cluster density and sentiment value. Our findings reveal that the emotional atmosphere in short video comments indicates continuity and clustering traits. Furthermore, a correlation was found between cluster size and emotional valence, with the relationship trend varying between macro- and micro-clusters. Regarding theoretical contributions, this study enriches the theory of emotional atmosphere by proposing a mathematical model of emotional atmosphere that combines machine learning and complex network analysis, and introduces cluster density and emotional value calculation methods. Concurrently, this study highlights emotion’s importance in short video comments, which can enhance disaster response efforts in the future.