Mining Multiplatform Opinions During Public Health Crisis: A Comparative Study

Kun Sun, Tian-Fang Zhao, Xiaokun Wu, Liang Yang, Di Jin, Wei-Neng Chen

Abstract: Emerging infectious diseases pose a growing threat to human society and have sparked extensive public discussions on social media. Although numerous efforts have been made in health data mining on social media, there is a lack of focus on quantitative comparisons across multiple platforms, despite their crucial role in the holistic social communication system. This study addresses this gap by developing a generalized regression model that considers the distinct attributes of social media platforms, including short-text, long-text, and Eastern or Western orientation. Using Monkeypox as an application case, this study examines differences among platforms based on four factors: user characteristics, text topics, text emotion, and text quality. The modeling and regression results reveal significant heterogeneity in public opinion expressions across different platforms, particularly between long-text and short-text platforms. Users on short-text platforms are more exposed to diverse information and tend to be susceptible to emotionally provocative content. On the other hand, users on long-text platforms prefer in-depth discussions and show greater receptivity to content infused with positive emotions. This study reveals the information bias brought by platform differences and contributes to data-driven modeling in social communication systems.