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 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 2024 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 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 2024 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