Published on in Vol 18, No 3 (2016): March

Understanding Online Health Groups for Depression: Social Network and Linguistic Perspectives

Understanding Online Health Groups for Depression: Social Network and Linguistic Perspectives

Understanding Online Health Groups for Depression: Social Network and Linguistic Perspectives

Authors of this article:

Ronghua Xu 1, 2 Author Orcid Image ;   Qingpeng Zhang 1, 2 Author Orcid Image

Journals

  1. Moyano A, Sicilia M, Barriocanal E. On the Graph Structure of the Web of Data. International Journal on Semantic Web and Information Systems 2018;14(2):70 View
  2. Moessner M, Feldhege J, Wolf M, Bauer S. Analyzing big data in social media: Text and network analyses of an eating disorder forum. International Journal of Eating Disorders 2018;51(7):656 View
  3. Yao X, Yu G, Tian X, Tang J. Patterns and Longitudinal Changes in Negative Emotions of People with Depression on Sina Weibo. Telemedicine and e-Health 2020;26(6):734 View
  4. Li Y, Cai M, Qin S, Lu X. Depressive Emotion Detection and Behavior Analysis of Men Who Have Sex With Men via Social Media. Frontiers in Psychiatry 2020;11 View
  5. Shrestha A, Serra E, Spezzano F. Multi-modal social and psycho-linguistic embedding via recurrent neural networks to identify depressed users in online forums. Network Modeling Analysis in Health Informatics and Bioinformatics 2020;9(1) View
  6. Wang X, Zhao K, Zhou X, Street N. Predicting User Posting Activities in Online Health Communities with Deep Learning. ACM Transactions on Management Information Systems 2020;11(3):1 View
  7. Bailey S, Zhang Y, Ramesh A, Golbeck J, Getoor L. A Structured and Linguistic Approach to Understanding Recovery and Relapse in AA. ACM Transactions on the Web 2021;15(1):1 View
  8. Baptista N, Pinho J, Alves H. Social Marketing and Online Social Support Structure in Contexts of Treatment Uncertainty. Journal of Nonprofit & Public Sector Marketing 2022;34(3):311 View
  9. Yao X, Yu G, Tang J, Zhang J. Extracting depressive symptoms and their associations from an online depression community. Computers in Human Behavior 2021;120:106734 View
  10. Ramamoorthy T, Karmegam D, Mappillairaju B. Use of social media data for disease based social network analysis and network modeling: A Systematic Review. Informatics for Health and Social Care 2021;46(4):443 View
  11. HUANG G, ZHOU X. The linguistic patterns of depressed patients. Advances in Psychological Science 2021;29(5):838 View
  12. Ghosh S, Anwar T. Depression Intensity Estimation via Social Media: A Deep Learning Approach. IEEE Transactions on Computational Social Systems 2021;8(6):1465 View
  13. Storman D, Jemioło P, Swierz M, Sawiec Z, Antonowicz E, Prokop-Dorner A, Gotfryd-Burzyńska M, Bala M. Meeting the Unmet Needs of Individuals With Mental Disorders: Scoping Review on Peer-to-Peer Web-Based Interactions. JMIR Mental Health 2022;9(12):e36056 View
  14. Belz F, Adair K, Proulx J, Frankel A, Sexton J. The language of healthcare worker emotional exhaustion: A linguistic analysis of longitudinal survey. Frontiers in Psychiatry 2022;13 View
  15. Zhong D, Liu C, Luan C, Li W, Cui J, Shi H, Zhang Q. Mental health problems among healthcare professionals following the workplace violence issue-mediating effect of risk perception. Frontiers in Psychology 2022;13 View
  16. Pan W, Wang X, Zhou W, Hang B, Guo L. Linguistic Analysis for Identifying Depression and Subsequent Suicidal Ideation on Weibo: Machine Learning Approaches. International Journal of Environmental Research and Public Health 2023;20(3):2688 View
  17. Shi J, Khoo Z. Online health community for change: Analysis of self-disclosure and social networks of users with depression. Frontiers in Psychology 2023;14 View
  18. Hards E, Orchard F, Reynolds S. ‘I am tired, sad and kind’: self-evaluation and symptoms of depression in adolescents. Child and Adolescent Psychiatry and Mental Health 2023;17(1) View
  19. Lu Y, Wang X, Su L, Zhao H. Multiplex Social Network Analysis to Understand the Social Engagement of Patients in Online Health Communities. Mathematics 2023;11(21):4412 View
  20. Shi J, Khoo Z. Words for the hearts: a corpus study of metaphors in online depression communities. Frontiers in Psychology 2023;14 View
  21. Humayun M, Brouillette M, Fellows L, Mayo N. The Patient Generated Index (PGI) as an early-warning system for predicting brain health challenges: a prospective cohort study for people living with Human Immunodeficiency Virus (HIV). Quality of Life Research 2023;32(12):3439 View
  22. Wang X, Zhao K, Amato M, Stanton C, Shuter J, Graham A. The Role of Seed Users in Nurturing an Online Health Community for Smoking Cessation Among People With HIV/AIDS. Annals of Behavioral Medicine 2024;58(2):122 View
  23. Li S, Pan W, Yip P, Wang J, Zhou W, Zhu T. Uncovering the heterogeneous effects of depression on suicide risk conditioned by linguistic features: A double machine learning approach. Computers in Human Behavior 2024;152:108080 View
  24. Wu X, Zhou Y, Zhong B. Measuring social support for depression on social media: A multifaceted study on user interaction and emotional spread. Telematics and Informatics 2024;89:102120 View
  25. Zhang Z, Li Z, Zhu J, Guo Z, Shi B, Hu B. Enhancing user sequence representation with cross-view collaborative learning for depression detection on Sina Weibo. Knowledge-Based Systems 2024;293:111650 View

Books/Policy Documents

  1. Xu R, Zhou J, Zhang Q, Hendler J. Encyclopedia of Social Network Analysis and Mining. View
  2. Xu R, Zhou J, Zhang Q, Hendler J. Encyclopedia of Social Network Analysis and Mining. View
  3. Gui T, Zhang Q, Zhu L, Zhou X, Peng M, Huang X. Chinese Computational Linguistics. View
  4. Anbalagan B. Proceedings of 2nd International Conference on Artificial Intelligence: Advances and Applications. View
  5. Haldorai A, R B, Murugan S, Balakrishnan M. Artificial Intelligence for Sustainable Development. View