Functional profiling of authoritative breast cancer-related key opinion leaders based on topic distributions: A comparative cluster analysis across social media platforms.

Objective: This study aimed to develop a functional typology of authoritative breast cancer-related key opinion leaders (ABKOLs) on Chinese social media and to examine how platform-specific dynamics shape their content strategies and audience emotional responses. Methods: Videos from selected ABKOL accounts on Douyin and Xiaohongshu were transcribed using automated speech recognition and processed through natural language cleaning. Latent Dirichlet Allocation was used to extract semantic themes, K-means clustering was applied to identify functional types, and sentiment analysis was conducted to assess emotional patterns in user comments. Results: A total of 19,960 videos from 30 ABKOLs were collected (17,302 from Douyin, 2658 from Xiaohongshu), of which 19,043 valid transcripts were retained for analysis. Four functional types were identified: preventive advocates (Cluster 1, Conclusion: This study revealed the functional roles, platform distributions, and emotional impacts of various ABKOL types. The findings underscore the importance of aligning content structures and emotional narratives with platform algorithms and audience expectations to optimize the effectiveness of social media-based cancer communication.