Evidence map›Paper›PMID 41710760›Full record

ArticleDigital health

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

Hongyan Du, Lei Gu

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Hongyan DuSchool of Foreign Languages, Southwest Jiaotong University, Chengdu, China.ORCID https://orcid.org/0000-0002-2907-1908
Lei GuSchool of Foreign Languages, Southwest Jiaotong University, Chengdu, China.ORCID https://orcid.org/0009-0009-5721-7669

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: 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. Methods: 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. Results: 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. Conclusion: 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.

Indexed as

audience engagementChinese social mediaDepression self-disclosuremental health discoursesentiment analysis

Identifiers

PMID41710760
PMCPMC12909761

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.