How depression self-disclosure shapes engagement and discourse on Chinese social media

This study investigates how sentiments in depression self-disclosures by everyday users on Chinese social media influence audience engagement behaviors and discourse themes, specifically examining the role of cultural context and platform affordances in non-Western mental health communication.

Adopting a mixed-methods approach, the study analyzed 535 posts and 17,301 comments from Xiaohongshu (a prominent lifestyle-sharing platform in China). A fine-tuned BERT model quantified sentiment, while multilevel regression models assessed the impact of sentiment on likes, favorites, shares, and comments. Semantic network analysis was employed to map thematic structures within audience responses.

Less positive sentiments significantly predicted higher engagement in likes and comments, indicating a community preference for authentic vulnerability over positivity. Sentiment did not predict favorites or shares, a finding attributed to cultural norms discouraging the publicization of "family ugliness". Notably, neutral posts generated higher engagement than positive ones, serving as safe grounds for advice-seeking. Thematic analysis revealed consistent clusters across sentiment categories: coping/resilience, emotional struggles, and health-related concerns.

Challenging the assumption that positivity drives support, this study demonstrates that negative self-disclosure fosters deeper engagement and validation in Chinese online communities. While cultural stigma suppresses public sharing, audiences actively utilize comments to provide a stabilized "protocol of care", suggesting that authentic vulnerability is a potent driver of stigma reduction and peer support.