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

Journals
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Li A, Jiao D, Zhu T. Detecting depression stigma on social media: A linguistic analysis. Journal of Affective Disorders 2018;232:358 View
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Acuña Caicedo R, Gómez Soriano J, Melgar Sasieta H. Bootstrapping semi-supervised annotation method for potential suicidal messages. Internet Interventions 2022;28:100519 View
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Kodati D, Tene R. Emotion mining for early suicidal threat detection on both social media and suicide notes using context dynamic masking-based transformer with deep learning. Multimedia Tools and Applications 2024;84(13):11729 View
- Zhang D, Zhou L, Tao J, Zhu T, Gao G. KETCH: A Knowledge-Enhanced Transformer-Based Approach to Suicidal Ideation Detection from Social Media Content. Information Systems Research 2025;36(1):572 View
- Li A. Predicting negative attitudes towards suicide in social media texts: prediction model development and validation study. Frontiers in Public Health 2024;12 View
- Huang C, Shaw F, Hsu W, Yu H, Chang S, Li M. Mindsets of suicide trajectories: An Linguistic Inquiry and Word Count analysis of suicide hotline conversations. Suicide and Life-Threatening Behavior 2024;54(6):1101 View
- Wang J, Jin X. Commentary: Psychometric properties of the modified Suicide Stroop Task (M-SST) in patients with suicide risk and healthy controls. Frontiers in Psychology 2024;15 View
- Ammerman B, McClure K, Law K, O'Loughlin C, Jacobucci R. Online disclosure of suicide method: What can online posts tell us about suicidal planning?. Journal of Psychiatric Research 2025;181:503 View
- Ammerman B, Kleiman E, O’Brien C, Knorr A, Bell K, Ram N, Robinson T, Reeves B, Jacobucci R. Smartphone-based text obtained via passive sensing as it relates to direct suicide risk assessment. Psychological Medicine 2025;55 View
- Dai Y, Liu J, Cao L, Xue Y, Wang X, Ding Y, Tian J, Feng L. Leveraging Social Media for Real-Time Interpretable and Amendable Suicide Risk Prediction With Human-in-The-Loop. IEEE Transactions on Affective Computing 2025;16(2):1128 View
Books/Policy Documents
- Huang Y, Liu X, Zhu T. Human Centered Computing. View
- Su Y, Zheng H, Liu X, Zhu T. Human Centered Computing. View
- Zhu S, Wang X, Liu P. Chinese Lexical Semantics. View
- Guo L, Xia L, Huang X, Fu Y, Li X, Zhou S, Zhao C, Yang B. Health Information Science. View
- Wongkoblap A, Vadillo M, Curcin V. Mental Health in a Digital World. View
- Liang Z, Liu D, Wan Q, Liu X, Liao G, Wan C. Social Media Processing. View
- Zhang S, Li S, Zhang T, Zhang X, Geng Y, Lin H, Luo L, Yang L. Health Information Processing. View
- ShimpyGoyal , Pathak N, Sharma N, Sahoo D, Dixit M. Data Mining and Information Security. View
Conference Proceedings
- Gaur M, Kursuncu U, Alambo A, Sheth A, Daniulaityte R, Thirunarayan K, Pathak J. Proceedings of the 27th ACM International Conference on Information and Knowledge Management. "Let Me Tell You About Your Mental Health!" View
- Chancellor S, Birnbaum M, Caine E, Silenzio V, De Choudhury M. Proceedings of the Conference on Fairness, Accountability, and Transparency. A Taxonomy of Ethical Tensions in Inferring Mental Health States from Social Media View
- Huang X, Xing L, Brubaker J, Paul M. 2017 IEEE International Conference on Healthcare Informatics (ICHI). Exploring Timelines of Confirmed Suicide Incidents Through Social Media View
- Stapleton L, Liu S, Liu C, Hong I, Chancellor S, Kraut R, Zhu H. Proceedings of the CHI Conference on Human Factors in Computing Systems. "If This Person is Suicidal, What Do I Do?": Designing Computational Approaches to Help Online Volunteers Respond to Suicidality View
- Sonawane J, Jain D. 2024 IEEE 3rd World Conference on Applied Intelligence and Computing (AIC). Approaches for Identifying Suicide Ideation in Social Media Texts: Comprehensive Review View
