Published on in Vol 2, No 2 (2015): April-June

Identifying Chinese Microblog Users With High Suicide Probability Using Internet-Based Profile and Linguistic Features: Classification Model

Identifying Chinese Microblog Users With High Suicide Probability Using Internet-Based Profile and Linguistic Features: Classification Model

Identifying Chinese Microblog Users With High Suicide Probability Using Internet-Based Profile and Linguistic Features: Classification Model

Journals

  1. Cheng Q, Kwok C, Zhu T, Guan L, Yip P. Suicide Communication on Social Media and Its Psychological Mechanisms: An Examination of Chinese Microblog Users. International Journal of Environmental Research and Public Health 2015;12(9):11506 View
  2. Fodeh S, Boudreaux E, Wang R, Silva D, Bossarte R, Goulet J, Brandt C, Altalib H. Suicide Risk on Twitter. International Journal of Knowledge Discovery in Bioinformatics 2018;8(2):1 View
  3. Braithwaite S, Giraud-Carrier C, West J, Barnes M, Hanson C. Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality. JMIR Mental Health 2016;3(2):e21 View
  4. Wang Z, Yu G, Tian X. Exploring Behavior of People with Suicidal Ideation in a Chinese Online Suicidal Community. International Journal of Environmental Research and Public Health 2018;16(1):54 View
  5. Burke T, Ammerman B, Jacobucci R. The use of machine learning in the study of suicidal and non-suicidal self-injurious thoughts and behaviors: A systematic review. Journal of Affective Disorders 2019;245:869 View
  6. Haines-Delmont A, Chahal G, Bruen A, Wall A, Khan C, Sadashiv R, Fearnley D. Testing Suicide Risk Prediction Algorithms Using Phone Measurements With Patients in Acute Mental Health Settings: Feasibility Study. JMIR mHealth and uHealth 2020;8(6):e15901 View
  7. Yin Z, Sulieman L, Malin B. A systematic literature review of machine learning in online personal health data. Journal of the American Medical Informatics Association 2019;26(6):561 View
  8. Li A, Jiao D, Liu X, Sun J, Zhu T. A Psycholinguistic Analysis of Responses to Live-Stream Suicides on Social Media. International Journal of Environmental Research and Public Health 2019;16(16):2848 View
  9. Zu X, Diao X, Meng Z. The impact of social media input intensity on firm performance: Evidence from Sina Weibo. Physica A: Statistical Mechanics and its Applications 2019;536:122556 View
  10. Li A, Jiao D, Zhu T. Detecting depression stigma on social media: A linguistic analysis. Journal of Affective Disorders 2018;232:358 View
  11. Cheng Q, Li T, Kwok C, Zhu T, Yip P. Assessing Suicide Risk and Emotional Distress in Chinese Social Media: A Text Mining and Machine Learning Study. Journal of Medical Internet Research 2017;19(7):e243 View
  12. Coşkun M, Ozturan M. #europehappinessmap: A Framework for Multi-Lingual Sentiment Analysis via Social Media Big Data (A Twitter Case Study). Information 2018;9(5):102 View
  13. Liu X, Liu X, Sun J, Yu N, Sun B, Li Q, Zhu T. Proactive Suicide Prevention Online (PSPO): Machine Identification and Crisis Management for Chinese Social Media Users With Suicidal Thoughts and Behaviors. Journal of Medical Internet Research 2019;21(5):e11705 View
  14. Khasawneh A, Chalil Madathil K, Dixon E, Wiśniewski P, Zinzow H, Roth R. Examining the Self-Harm and Suicide Contagion Effects of the Blue Whale Challenge on YouTube and Twitter: Qualitative Study. JMIR Mental Health 2020;7(6):e15973 View
  15. Liu D, Fu Q, Wan C, Liu X, Jiang T, Liao G, Qiu X, Liu R. Suicidal Ideation Cause Extraction From Social Texts. IEEE Access 2020;8:169333 View
  16. Wongkoblap A, Vadillo M, Curcin V. Researching Mental Health Disorders in the Era of Social Media: Systematic Review. Journal of Medical Internet Research 2017;19(6):e228 View
  17. Chancellor S, Baumer E, De Choudhury M. Who is the "Human" in Human-Centered Machine Learning. Proceedings of the ACM on Human-Computer Interaction 2019;3(CSCW):1 View
  18. Aladağ A, Muderrisoglu S, Akbas N, Zahmacioglu O, Bingol H. Detecting Suicidal Ideation on Forums: Proof-of-Concept Study. Journal of Medical Internet Research 2018;20(6):e215 View
  19. Bernert R, Hilberg A, Melia R, Kim J, Shah N, Abnousi F. Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations. International Journal of Environmental Research and Public Health 2020;17(16):5929 View
  20. Hettige N, Nguyen T, Yuan C, Rajakulendran T, Baddour J, Bhagwat N, Bani-Fatemi A, Voineskos A, Mallar Chakravarty M, De Luca V. Classification of suicide attempters in schizophrenia using sociocultural and clinical features: A machine learning approach. General Hospital Psychiatry 2017;47:20 View
  21. Wang X, Chen S, Li T, Li W, Zhou Y, Zheng J, Chen Q, Yan J, Tang B. Depression Risk Prediction for Chinese Microblogs via Deep-Learning Methods: Content Analysis. JMIR Medical Informatics 2020;8(7):e17958 View
  22. Taylor J, Pagliari C. Mining social media data: How are research sponsors and researchers addressing the ethical challenges?. Research Ethics 2018;14(2):1 View
  23. Chancellor S, De Choudhury M. Methods in predictive techniques for mental health status on social media: a critical review. npj Digital Medicine 2020;3(1) View
  24. Liu L, Li T, Teo A, Kato T, Wong P. Harnessing Social Media to Explore Youth Social Withdrawal in Three Major Cities in China: Cross-Sectional Web Survey. JMIR Mental Health 2018;5(2):e34 View
  25. Acuña Caicedo R, Gómez Soriano J, Melgar Sasieta H. Assessment of supervised classifiers for the task of detecting messages with suicidal ideation. Heliyon 2020;6(8):e04412 View
  26. Bruen A, Wall A, Haines-Delmont A, Perkins E. Exploring Suicidal Ideation Using an Innovative Mobile App-Strength Within Me: The Usability and Acceptability of Setting up a Trial Involving Mobile Technology and Mental Health Service Users. JMIR Mental Health 2020;7(9):e18407 View
  27. Ji S, Pan S, Li X, Cambria E, Long G, Huang Z. Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications. IEEE Transactions on Computational Social Systems 2021;8(1):214 View
  28. Jacobucci R, Ammerman B, Tyler Wilcox K. The use of text‐based responses to improve our understanding and prediction of suicide risk. Suicide and Life-Threatening Behavior 2021;51(1):55 View
  29. Rassy J, Bardon C, Dargis L, Côté L, Corthésy-Blondin L, Mörch C, Labelle R. Information and Communication Technology Use in Suicide Prevention: Scoping Review. Journal of Medical Internet Research 2021;23(5):e25288 View
  30. Acuña Caicedo R, Gómez Soriano J, Melgar Sasieta H. Bootstrapping semi-supervised annotation method for potential suicidal messages. Internet Interventions 2022;28:100519 View
  31. Bonifazi G, Cecchini S, Corradini E, Giuliani L, Ursino D, Virgili L. Extracting time patterns from the lifespans of TikTok challenges to characterize non-dangerous and dangerous ones. Social Network Analysis and Mining 2022;12(1) View
  32. Yang B, Xia L, Liu L, Nie W, Liu Q, Li X, Ao M, Wang X, Xie Y, Liu Z, Huang Y, Huang Z, Gong X, Luo D. A Suicide Monitoring and Crisis Intervention Strategy Based on Knowledge Graph Technology for “Tree Hole” Microblog Users in China. Frontiers in Psychology 2021;12 View
  33. Xu X. Detecting Suicide Ideation in the Online Environment: A Survey of Methods and Challenges. IEEE Transactions on Computational Social Systems 2022;9(3):679 View
  34. Lao C, Lane J, Suominen H. Analyzing Suicide Risk From Linguistic Features in Social Media: Evaluation Study. JMIR Formative Research 2022;6(8):e35563 View
  35. Wang R, Yang B, Ma Y, Wang P, Yu Q, Zong X, Huang Z, Ma S, Hu L, Hwang K, Liu Z. Medical-Level Suicide Risk Analysis: A Novel Standard and Evaluation Model. IEEE Internet of Things Journal 2021;8(23):16825 View
  36. Cao L, Zhang H, Feng L. Building and Using Personal Knowledge Graph to Improve Suicidal Ideation Detection on Social Media. IEEE Transactions on Multimedia 2022;24:87 View
  37. Taghvaei N, Masoumi B, Keyvanpour M. Analytical framework for mental health feature extraction methods in social networks. Intelligent Decision Technologies 2021;15(3):343 View
  38. Safa R, Bayat P, Moghtader L. Automatic detection of depression symptoms in twitter using multimodal analysis. The Journal of Supercomputing 2022;78(4):4709 View
  39. Zhao Y, Liu D, Wan C, Liu X, Qiu X, Nie J. Find Supports for the Post about Mental Issues: More Than Semantic Matching. ACM Transactions on Asian and Low-Resource Language Information Processing 2022;21(6):1 View
  40. Cao L, Zhang H, Wang X, Feng L. Learning Users Inner Thoughts and Emotion Changes for Social Media Based Suicide Risk Detection. IEEE Transactions on Affective Computing 2023;14(2):1280 View
  41. Geng S, He Y, Duan L, Yang C, Wu X, Liang G, Niu B. The Association Between Linguistic Characteristics of Physicians’ Communication and Their Economic Returns: Mixed Method Study. Journal of Medical Internet Research 2024;26:e42850 View

Books/Policy Documents

  1. Huang Y, Liu X, Zhu T. Human Centered Computing. View
  2. Su Y, Zheng H, Liu X, Zhu T. Human Centered Computing. View
  3. Zhu S, Wang X, Liu P. Chinese Lexical Semantics. View
  4. Guo L, Xia L, Huang X, Fu Y, Li X, Zhou S, Zhao C, Yang B. Health Information Science. View
  5. Wongkoblap A, Vadillo M, Curcin V. Mental Health in a Digital World. View
  6. Liang Z, Liu D, Wan Q, Liu X, Liao G, Wan C. Social Media Processing. View