关键词: 用户生成内容（UGC）平台, BERTopic, 用户参与行为, 小红书, 影响因素, 信息行为

Abstract:

[Purpose/Significance] This study focuses on the participation behavior of users prone to depression who participate in user-generated content (UGC) platforms, aiming to explore their behavioral heterogeneity and the underlying influencing mechanisms. The research aims to expand the theoretical scope of studies on user behavior while providing UGC platforms with practical guidance on building differentiated user care models and refining operational strategies. By utilizing authentic user-generated content as the data foundation, this study addresses the representational limitations commonly associated with traditional small-sample approaches, such as surveys and interviews. It introduces a data-driven perspective and methodological innovation to the field of information behavior research. Furthermore, this study enhances the understanding of varying psychological and behavioral needs among different types of depression-prone users. The findings can assist platforms in optimizing user experience, improving emotional support systems within online communities, and informing the development of more targeted and responsive intervention strategies. [Method/Process] First, web scraping techniques were used to collect a large volume of depression-related posts from the Xiaohongshu platform as the primary data source. Second, representative keywords were extracted through Word2Vec and K-means clustering algorithms. A keyword co-occurrence network was then constructed using the Leiden clustering algorithm to identify semantic relationships. By integrating user attribute information, the study achieved a fine-grained classification of heterogeneous depression-prone user groups. Third, drawing on self-determination theory (SDT) and the technology acceptance model (TAM), and leveraging BERTopic for advanced topic modeling, the study constructed a comprehensive factor model to examine the mechanisms influencing user participation behavior in depth. [Results/Conclusions] The research identifies three distinct types of depression-prone users: adolescent depression expression, help-seeking expression, and emotional breakdown expression. Results indicate that posting and commenting behaviors across these groups are primarily driven by emotional needs and environmental factors. Emotional needs are the dominant motivator for active participation, while environmental influences significantly contribute to triggering interaction, especially within comment sections. Additionally, adolescent depression expression and emotional breakdown expression show stronger tendencies toward self-related needs, reflecting deeper emotional and identity concerns. In contrast, help-seeking expression exhibit more evident competence-related needs, focusing on practical advice and problem-solving. Although competence and technical factors account for a smaller proportion, they still play a meaningful supporting role in shaping the structure and substance of user participation behavior on UGC platforms.

Key words: User-Generated Content (UGC), BERTopic, user participatory behavior, Xiaohongshu, influencing factors, information behavior