Evidence map›Paper›PMID 42582515›Full record

ArticleFrontiers in psychology2026

Comparative sentiment and thematic analysis of depression-related discourse between healthcare professionals and non-healthcare individuals on Chinese social media: a computational text analysis study.

Qi Zhou, Yuxi Chen, Yi Li, Lili Yang, Qinrou Yu, Lihui Yan, Danhong Qian

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Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Qi ZhouHangzhou Linping District Integrated Traditional Chinese and Western Medicine Hospital, Hangzhou, China.
Yuxi ChenHangzhou Linping District Integrated Traditional Chinese and Western Medicine Hospital, Hangzhou, China.
Yi LiMedicine department, Huzhou University, Hangzhou, Zhejiang, China.
Lili YangNursing education department, Sir Run Run Shaw Hospital of Zhejiang University, Hangzhou, China.
Qinrou YuNursing department, Hangzhou Normal University, Hangzhou, Zhejiang, China.
Lihui YanHangzhou Linping District Integrated Traditional Chinese and Western Medicine Hospital, Hangzhou, China.
Danhong QianHangzhou Linping District Integrated Traditional Chinese and Western Medicine Hospital, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study analyzes differences in depression-related expressions between healthcare professionals and non-healthcare individuals on Chinese social media. Background: Sentiment plays a critical role in depression management, influencing condition assessment, social support, and treatment adherence. However, traditional research methods are limited by self-report bias, small samples, and constrained expression. Social media offers a unique platform to capture authentic emotions at scale. Methods: A total of 120,895 comments were retrieved from Zhihu, Weibo, and Xiaohongshu. Data underwent cleaning, relevance verification, and anonymization. Users were classified into healthcare professionals and non-healthcare individuals through profile verification and manual annotation. Sentiment analysis applied the Dalian University of Technology Sentiment Lexicon, adapted to Chinese contexts and grounded in Ekman's emotional framework (anger, disgust, fear, sadness, surprise, positive emotion). Polarity and intensity (-1 to 1) were quantified. Content analysis employed a K-Means clustering algorithm to identify thematic patterns. Content analysis was conducted using computational text analysis methods, including sentiment analysis and K-means clustering, to identify emotional and thematic patterns in depression-related social media discourse. Results: Non-healthcare individuals expressed a wider emotional spectrum (anger, disgust, fear, sadness, surprise, and positive emotions), while healthcare professionals primarily expressed anger, fear, and positive emotions. Non-healthcare discourse focused on illness experiences, stigma, and treatment conflict, whereas healthcare professionals emphasized occupational stress, emotional labor, and professional fulfillment. Conclusions: The results highlight divergent emotional orientations: patients and the public center on illness experiences, while professionals emphasize identity and occupational strain. These differences underscore the need for tailored communication strategies, empathy training, and institutional psychological support for healthcare providers.

Indexed as

depressionK-means algorithmnursingsentiment analysissocial media

Identifiers

PMID42582515
PMCPMC13457727

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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.