Analyzing group polarization through text emotion measurement and time series prediction: A comparative study across three online platforms

This study investigated the emotional trends of users on social platforms, considering the event of “the Changsha girl jumping off the Lalamove truck” as a case study. It examined the effects of recommendation algorithms and group social comparison attributes on group emotions across three platforms: Zhihu, Weibo, and Bilibili. Through text mining and emotion analysis algorithms, group reviews were analyzed, and an event-based ARIMA robustness detection model was constructed using time series data. Utilizing the theoretical framework of the social comparison process, the study discovered that the “information database” formed by the recommendation algorithms of social platforms fosters the emergence of emotional group polarization among users. Furthermore, the findings revealed that the audience's social comparison attributes play a role in shaping emotional group polarization. High knowledge attributes tend to inhibit emotional group polarization, while low knowledge attributes tend to promote it. Machine learning algorithms were employed to measure user sentiment in social media platform comments, revealing insights into the causes of group polarization through a comparison of social comparison attributes and algorithm techniques. Future studies must focus on measuring technical information entropy.