Exploring the Dynamic Interplay of User Characteristics and Topic Influence on Weibo: A Comprehensive Analysis

In the contemporary digital landscape, Weibo has emerged as a dominant force, profoundly shaping societal dynamics and communication paradigms. This study investigates the intricate interplay between user characteristics and topic influence on Weibo, addressing a critical gap in existing research. Leveraging cutting-edge techniques such as user group segmentation, Dynamic Topic Modeling (DTM), and Jensen-Shannon divergence, our research delves into the evolving landscape of Weibo users. The study begins by highlighting the staggering growth of Weibo, with 462 million monthly active users and 130 million daily text exchanges, emphasizing the need for updated insights since 2018. Traditional research in this domain has focused on personalized recommendations, sentiment analysis, and user behavior, often relying on keyword extraction. However, this study challenges conventional approaches by thoroughly analyzing attribute features, interaction dynamics, and content, classifying Weibo users into distinct groups based on nuanced characteristics. A novel taxonomy for interest extraction is introduced, considering the temporal dimension and assessing the impact of various topics on Weibo coverage and user activity. This comprehensive approach not only enhances our understanding of Weibo users but also provides a framework for future research in digital communication platforms. Theoretical implications are profound, as this study reveals the existence of specific user typologies with evolving thematic preferences, challenging the traditional notion of categorizing users as a single group. Managerial insights benefit social media administrators, content creators, and marketers equally significantly. By identifying user groups and their changing interests, administrators can enhance user experiences, while content creators and marketers can tailor their strategies for more effective communication. This study unravels concealed patterns in Weibo user groups, offering valuable insights for navigating the ever-changing landscape of online communities, contributing to academic understanding, and providing practical guidance for managing diverse user communities on social media platforms like Weibo.