Evidence map›Paper›PMID 41721351›Full record

ArticleBMC public health2026

Emotional and sentiment analyses of MSM geo-social networking app users in Shenzhen: implications for mental health interventions.

Luyao Hu, Jinghua Li, Huiming Liu, Tiantian Fu, Fengsu Hou

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Article in BMC public health, 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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5 · Who and what money

Authors and funding

5 authors.

Luyao Hu *Department of Medical Statistics, School of Public Health, Sun Yat-Sen University, No. 74 Zhongshan 2Nd Road, Guangzhou, Guangdong, 510080, China.
Jinghua Li *Department of Public Health and Medicinal Administration, Faculty of Health Sciences, University of Macau, SAR, Avenida da Universidade, Taipa, Macao, China.
Huiming LiuDepartment of Public Health, Shenzhen Mental Health Center, Shenzhen Kangning Hospital, No.1080, Cuizhu Road, Shenzhen, Guangdong, 518020, China.
Tiantian FuDepartment of Medical Statistics, School of Public Health, Sun Yat-Sen University, No. 74 Zhongshan 2Nd Road, Guangzhou, Guangdong, 510080, China.
Fengsu HouSchool of Public Health, Shenzhen University Medical School, Shenzhen University, No.1066 Xueyuan Blvd, Shenzhen, Guangdong, 518020, China. fengsu_hou@szu.edu.cn.

Funding

Natural Science Foundation of China 72374095Natural Science Foundation of Guangdong Province 2022A1515011533Shenzhen Fund for Guangdong Provincial High-level Clinical Key Specialties No.SZGSP013Start-up Research Grant of the University of Macau SRG2025-00031-FHS
6 · The paper itself

Abstract

backgroundMen who have sex with men (MSM) face distinct external and internal stressors that substantially affect their mental health, leading to higher rates of depression and engagement in risky behaviors. Geosocial networking apps provide new perspectives to assess and promote the mental health of MSM. This study aims to examine the emotions and sentiments of MSM in Shenzhen, China, using data from a Chinese MSM geosocial networking app, and to explore the characteristics, temporal trends, and associated factors of emotions and sentiments.

methodsThis cross-sectional study collected data from the geosocial networking app Blued using a web crawler, focusing on users in Shenzhen who posted more than 15 updates between September and October 2020. Natural language processing techniques, including word segmentation and sentiment analysis, were applied to assess emotional and sentiment patterns. Univariate and multivariate logistic regression analyses were used to identify associated factors with these patterns.

resultsThe study included 7,537 users with a mean age of 28.7. We found significant emotional fluctuations, particularly negative emotions, during early morning hours. Factors such as body mass index, social media engagement, and the frequency of health-related words were significantly associated with emotional and sentiment trends. Users who posted more frequently and had fewer blacklist entries exhibited more positive emotions.

conclusionsSocial media provides valuable insights into the mental health patterns of MSM. This study highlights the potential for real-time monitoring and targeted interventions tailored to the unique needs of sexual minorities. The findings underscore the importance of culturally relevant and LGBT-affirmative online mental health services.

Indexed as

EmotionsHomosexuality, MaleMental HealthMobile ApplicationsSocial NetworkingAdultChinaCross-Sectional StudiesHumansMaleYoung AdultEmotionGSNMSMSentiment analyses

Identifiers

PMID41721351
PMCPMC13032358

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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.