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PMID: Reviewed by Sarah Bassett and H. Author information Article notes Copyright and License information Disclaimer. Corresponding author. Corresponding Author: Chun Hao nc. This article has been corrected. See J Med Internet Res. Multimedia Appendix 2. Multimedia Appendix 3. Multimedia Appendix 4.

Abstract Background Affective states, including sentiment and emotion, are critical determinants of health. Objective This study aimed to understand and examine the associations of affective states with sexual behaviors and health status among MSM using public data from the Blued Blued International Inc app. Methods A total of , public postings of , MSM users located in Guangdong were saved from the Blued app by automatic screen capture.

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Results A total of active MSM users and their , postings were finally selected. Conclusions The MSM social media community mainly expressed their positive affect in the early morning and negative affect after midnight. Keywords: affect, men who have sex with men, sexual behaviors, health status, social media. Introduction Background Affective states, including emotion and sentiment, can be defined as positive or negative evaluations of objects, behaviors, or thoughts [ 1 ].

Objectives Therefore, this study was designed to explore affective states, including sentiment and emotions, and their associations with sexual behaviors and health status among Chinese MSM by analyzing user-generated data in Blued. Open in a separate window. Figure 1. Measures Demographic and Social Network Characteristics Demographic characteristics, including age, educational level, geolocation, hometown, height, weight, and sex role, were obtained from the user profile database. Affective States: Sentiment and Emotion Overview To measure affective states, we first calculated the probability of words related to affect across all postings for each user in a given hour P u,h.

Step 2 The probability of depression and anxiety is higher for MSM than the general population. Step 5 Finally, we calculated the general level of the relative probability affect score as defined in equation 6 , where U h is the subset of users who were active during hour h [ 40 ]. Sexual Behaviors and Health Status Sexual behavior—related words eg, sex, condom, and kiss and health-related words eg, infection, insomnia, and exercise from SC-LIWC were applied to identify the words associated with sexual behaviors and health status in the postings see Table 1 [ 58 ].

Themes of Postings Understanding negative affect among MSM is critical for a mental health intervention or crisis interventions. Statistical Analysis First, mean and standard deviations were used to describe continuous variables. Results Demographic Characteristics A total of active MSM users on Blued from the Guangdong province and their , postings were finally selected. Affective States: Sentiment The mean SD scores of positive affect, negative affect, and positive sentiment score were 0. Figure 2. Affective States: Emotion The mean SD scores of joy, sadness, anger, fear, disgust, and positive emotion score were 0.

Figure 3. Sleep health or mental health. Social Network The mean SD scores of followers, followees, and chat groups were 2.

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Figure 4. Hourly changes in individual sexual behaviors, positive affect, and joy.


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Figure 5. Hourly changes in individual health status, negative affect, sadness, and disgust. Associations Between Affective States and Demographic Characteristics Multimedia Appendices 2 - 4 show the associations between affective states and demographic characteristics. Discussion Principal Findings This is one of the first studies to assess the affective states of MSM using social media data.

Limitations It is important to acknowledge the limitations of this study. Acknowledgments We are grateful to the participants of this study.

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Appendix Multimedia Appendix 1 Quoted vocabulary and responses in the original Chinese text. Click here to view. Multimedia Appendix 2 Univariate and multivariate analysis of sentiment, sexual behaviors, and health status.

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Multimedia Appendix 3 Univariate and multivariate analysis of emotions joy, sadness, and disgust , sexual behaviors, and health status. Multimedia Appendix 4 Univariate and multivariate analysis of emotions anger, fear, and positive emotion score , sexual behaviors, and health status. Footnotes Conflicts of Interest: None declared. References 1. Thoits PA. The sociology of emotions. Annu Rev Sociol. Desmet P. Designing emotions.

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Des J. Khosrow-Pour DB. Encyclopedia of Information Science and Technology. Fourth Edition. Broad CD. Emotion and sentiment. J Aesthet Art Crit. Stets JE. Emotions and sentiments. In: DeLamater J, editor.


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Handbook of Social Psychology. Boston, MA: Springer; Ben-Ze'ev A. The Subtlety of Emotions. Volkova S, Bachrach Y. On predicting sociodemographic traits and emotions from communications in social networks and their implications to online self-disclosure.


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Cyberpsychol Behav Soc Netw. Anas M, Akhouri D. J Indian Health Psychol. Positive affective processes underlie positive health behaviour change. Psychol Health. Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. An analysis of sleep complaints on Sina Weibo. Comput Hum Behav. Methods of using real-time social media technologies for detection and remote monitoring of HIV outcomes.

Prev Med. An analysis of anxiety-related postings on Sina Weibo. Ontology-based approach to social data sentiment analysis: detection of adolescent depression signals. Gibbs JJ, Rice E. The social context of depression symptomology in sexual minority male youth: determinants of depression in a sample of Grindr users. J Homosex. Relationship between the use of gay mobile phone applications and HIV infection among men who have sex with men in Ningbo, China: a cross-sectional study.