Published on in Vol 19, No 7 (2017): July

Assessing Suicide Risk and Emotional Distress in Chinese Social Media: A Text Mining and Machine Learning Study

Assessing Suicide Risk and Emotional Distress in Chinese Social Media: A Text Mining and Machine Learning Study

Assessing Suicide Risk and Emotional Distress in Chinese Social Media: A Text Mining and Machine Learning Study

Journals

  1. Kim H, Lee S, Lee S, Hong S, Kang H, Kim N. Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone. JMIR mHealth and uHealth 2019;7(10):e14149 View
  2. Pourmand A, Roberson J, Caggiula A, Monsalve N, Rahimi M, Torres-Llenza V. Social Media and Suicide: A Review of Technology-Based Epidemiology and Risk Assessment. Telemedicine and e-Health 2019;25(10):880 View
  3. 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
  4. Du J, Zhang Y, Luo J, Jia Y, Wei Q, Tao C, Xu H. Extracting psychiatric stressors for suicide from social media using deep learning. BMC Medical Informatics and Decision Making 2018;18(S2) View
  5. Giuntini F, Cazzolato M, dos Reis M, Campbell A, Traina A, Ueyama J. A review on recognizing depression in social networks: challenges and opportunities. Journal of Ambient Intelligence and Humanized Computing 2020;11(11):4713 View
  6. 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
  7. 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
  8. Schlichthorst M, King K, Turnure J, Sukunesan S, Phelps A, Pirkis J. Influencing the Conversation About Masculinity and Suicide: Evaluation of the Man Up Multimedia Campaign Using Twitter Data. JMIR Mental Health 2018;5(1):e14 View
  9. Notredame C, Morgiève M, Morel F, Berrouiguet S, Azé J, Vaiva G. Distress, Suicidality, and Affective Disorders at the Time of Social Networks. Current Psychiatry Reports 2019;21(10) View
  10. Pyenson B, Alston M, Gomberg J, Han F, Khandelwal N, Dei M, Son M, Vora J. Applying Machine Learning Techniques to Identify Undiagnosed Patients with Exocrine Pancreatic Insufficiency. Journal of Health Economics and Outcomes Research 2019;6(2):32 View
  11. 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
  12. Ortiz P, Khin Khin E. Traditional and new media's influence on suicidal behavior and contagion. Behavioral Sciences & the Law 2018;36(2):245 View
  13. Jasso-Medrano J, López-Rosales F. Measuring the relationship between social media use and addictive behavior and depression and suicide ideation among university students. Computers in Human Behavior 2018;87:183 View
  14. 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
  15. Ghani N, Hamid S, Targio Hashem I, Ahmed E. Social media big data analytics: A survey. Computers in Human Behavior 2019;101:417 View
  16. 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
  17. Liang Y, Zheng X, Zeng D. A survey on big data-driven digital phenotyping of mental health. Information Fusion 2019;52:290 View
  18. Saad J, Prochaska J. A philosophy of health: life as reality, health as a universal value. Palgrave Communications 2020;6(1) View
  19. 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
  20. Shatte A, Hutchinson D, Fuller-Tyszkiewicz M, Teague S. Social Media Markers to Identify Fathers at Risk of Postpartum Depression: A Machine Learning Approach. Cyberpsychology, Behavior, and Social Networking 2020;23(9):611 View
  21. Lopez‐Castroman J, Moulahi B, Azé J, Bringay S, Deninotti J, Guillaume S, Baca‐Garcia E. Mining social networks to improve suicide prevention: A scoping review. Journal of Neuroscience Research 2020;98(4):616 View
  22. Mavragani A. Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206 View
  23. LI L, WANG Z, ZHANG Q, WEN H. Effect of anger, anxiety, and sadness on the propagation scale of social media posts after natural disasters. Information Processing & Management 2020;57(6):102313 View
  24. Zheng Z, Yang Q, Liu Z, Qiu J, Gu J, Hao Y, Song C, Jia Z, Hao C. Associations Between Affective States and Sexual and Health Status Among Men Who Have Sex With Men in China: Exploratory Study Using Social Media Data. Journal of Medical Internet Research 2020;22(1):e13201 View
  25. 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
  26. Motti V, Kalantari N, Neris V. Understanding how social media imagery empowers caregivers: an analysis of microcephaly in Latin America. Personal and Ubiquitous Computing 2021;25(2):321 View
  27. Oyebode O, Alqahtani F, Orji R. Using Machine Learning and Thematic Analysis Methods to Evaluate Mental Health Apps Based on User Reviews. IEEE Access 2020;8:111141 View
  28. Cabrera D, Roy D, Chisolm M. Social Media Scholarship and Alternative Metrics for Academic Promotion and Tenure. Journal of the American College of Radiology 2018;15(1):135 View
  29. Yang T, Xie J, Li G, Mou N, Chen C, Zhao J, Liu Z, Lin Z. Traffic Impact Area Detection and Spatiotemporal Influence Assessment for Disaster Reduction Based on Social Media: A Case Study of the 2018 Beijing Rainstorm. ISPRS International Journal of Geo-Information 2020;9(2):136 View
  30. Day J, Freiberg K, Hayes A, Homel R. Towards Scalable, Integrative Assessment of Children’s Self-Regulatory Capabilities: New Applications of Digital Technology. Clinical Child and Family Psychology Review 2019;22(1):90 View
  31. Chan M, Li T, Law Y, Wong P, Chau M, Cheng C, Fu K, Bacon-Shone J, Cheng Q, Yip P, van Amelsvoort T. Engagement of vulnerable youths using internet platforms. PLOS ONE 2017;12(12):e0189023 View
  32. O’Connor R, Portzky G. Looking to the Future: A Synthesis of New Developments and Challenges in Suicide Research and Prevention. Frontiers in Psychology 2018;9 View
  33. Soron T. “I will kill myself” – The series of posts in Facebook and unnoticed departure of a life. Asian Journal of Psychiatry 2019;44:55 View
  34. Chen L, Hu N, Shu C, Chen X. Adult attachment and self-disclosure on social networking site: A content analysis of Sina Weibo. Personality and Individual Differences 2019;138:96 View
  35. Van den Nest M, Till B, Niederkrotenthaler T. Comparing Indicators of Suicidality Among Users in Different Types of Nonprofessional Suicide Message Boards. Crisis 2019;40(2):125 View
  36. 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
  37. Asongu S, Nwachukwu J, Orim S, Pyke C. Crime and social media. Information Technology & People 2019;32(5):1215 View
  38. Lee K, Lee D, Hong H. Text mining analysis of teachers’ reports on student suicide in South Korea. European Child & Adolescent Psychiatry 2020;29(4):453 View
  39. Fonseka T, Bhat V, Kennedy S. The utility of artificial intelligence in suicide risk prediction and the management of suicidal behaviors. Australian & New Zealand Journal of Psychiatry 2019;53(10):954 View
  40. Gooding P. Mapping the rise of digital mental health technologies: Emerging issues for law and society. International Journal of Law and Psychiatry 2019;67:101498 View
  41. 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
  42. Liang Y, Guo B, Yu Z, Zheng X, Wang Z, Tang L. A multi-view attention-based deep learning system for online deviant content detection. World Wide Web 2021;24(1):205 View
  43. LUO F, JIANG L, TIAN X, XIAO M, MA Y, ZHANG S. Shyness prediction and language style model construction of elementary school students. Acta Psychologica Sinica 2021;53(2):155 View
  44. Laacke S, Mueller R, Schomerus G, Salloch S. Artificial Intelligence, Social Media and Depression. A New Concept of Health-Related Digital Autonomy. The American Journal of Bioethics 2021;21(7):4 View
  45. Liang Y, Li H, Guo B, Yu Z, Zheng X, Samtani S, Zeng D. Fusion of heterogeneous attention mechanisms in multi-view convolutional neural network for text classification. Information Sciences 2021;548:295 View
  46. Cox C, Moscardini E, Cohen A, Tucker R. Machine learning for suicidology: A practical review of exploratory and hypothesis-driven approaches. Clinical Psychology Review 2020;82:101940 View
  47. Skaik R, Inkpen D. Using Social Media for Mental Health Surveillance. ACM Computing Surveys 2021;53(6):1 View
  48. Bauer B, Law K, Rogers M, Capron D, Bryan C. Editorial overview: Analytic and methodological innovations for suicide‐focused research. Suicide and Life-Threatening Behavior 2021;51(1):5 View
  49. 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
  50. Cheng Q, Lui C. Applying text mining methods to suicide research. Suicide and Life-Threatening Behavior 2021;51(1):137 View
  51. Mansourian M, Khademi S, Marateb H. A Comprehensive Review of Computer-Aided Diagnosis of Major Mental and Neurological Disorders and Suicide: A Biostatistical Perspective on Data Mining. Diagnostics 2021;11(3):393 View
  52. 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
  53. Kim J, Lee D, Park E. Machine Learning for Mental Health in Social Media: Bibliometric Study. Journal of Medical Internet Research 2021;23(3):e24870 View
  54. HUANG G, ZHOU X. The linguistic patterns of depressed patients. Advances in Psychological Science 2021;29(5):838 View
  55. Gooding P, Kariotis T. Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review. JMIR Mental Health 2021;8(6):e24668 View
  56. Liu X, Liu X. Online Suicide Identification in the Framework of Rhetorical Structure Theory (RST). Healthcare 2021;9(7):847 View
  57. Jung W, Kim D, Nam S, Zhu Y. Suicidality Detection on Social Media Using Metadata and Text Feature Extraction and Machine Learning. Archives of Suicide Research 2023;27(1):13 View
  58. Feldhege J, Wolf M, Moessner M, Bauer S. Psycholinguistic changes in the communication of adolescent users in a suicidal ideation online community during the COVID-19 pandemic. European Child & Adolescent Psychiatry 2023;32(6):975 View
  59. Yip P, Xiao Y, Xu Y, Chan E, Cheung F, Chan C, Pirkis J. Social Media Sentiments on Suicides at the New York City Landmark, Vessel: A Twitter Study. International Journal of Environmental Research and Public Health 2022;19(18):11694 View
  60. Fukazawa Y. Estimating Mental Health Using Human-generated Big Data and Machine Learning. The Brain & Neural Networks 2022;29(2):78 View
  61. Chen Y, Liu C, Du Y, Zhang J, Yu J, Xu H. Machine learning classification model using Weibo users' social appearance anxiety. Personality and Individual Differences 2022;188:111449 View
  62. Chen X, Mo Q, Yu B, Bai X, Jia C, Zhou L, Ma Z. Hierarchical and nested associations of suicide with marriage, social support, quality of life, and depression among the elderly in rural China: Machine learning of psychological autopsy data. Frontiers in Psychiatry 2022;13 View
  63. Homan S, Gabi M, Klee N, Bachmann S, Moser A, Duri' M, Michel S, Bertram A, Maatz A, Seiler G, Stark E, Kleim B. Linguistic features of suicidal thoughts and behaviors: A systematic review. Clinical Psychology Review 2022;95:102161 View
  64. Spilsbury J, Hernandez E, Kiley K, Gillerlane Hinkes E, Prasanna S, Shafiabadi N, Rao P, Sahoo S. Social Service Workers’ Use of Social Media to Obtain Client Information: Current Practices and Perspectives on a Potential Informatics Platform. Journal of Social Service Research 2022;48(6):739 View
  65. Kelley S, Mhaonaigh C, Burke L, Whelan R, Gillan C. Machine learning of language use on Twitter reveals weak and non-specific predictions. npj Digital Medicine 2022;5(1) View
  66. Pyenson B, Alston M, Gomberg J, Han F, Khandelwal N, Dei M, Son M, Vora J. Applying Machine Learning Techniques to Identify Undiagnosed Patients with Exocrine Pancreatic Insufficiency. Journal of Health Economics and Outcomes Research 2019:32 View
  67. Yang B, Chen P, Li X, Yang F, Huang Z, Fu G, Luo D, Wang X, Li W, Wen L, Zhu J, Liu Q. Characteristics of High Suicide Risk Messages From Users of a Social Network—Sina Weibo “Tree Hole”. Frontiers in Psychiatry 2022;13 View
  68. Zhang T, Schoene A, Ji S, Ananiadou S. Natural language processing applied to mental illness detection: a narrative review. npj Digital Medicine 2022;5(1) View
  69. Kmetty Z, Bozsonyi K. Identifying Depression-Related Behavior on Facebook—An Experimental Study. Social Sciences 2022;11(3):135 View
  70. 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
  71. Kruzan K, Bazarova N, Whitlock J. Investigating Self-injury Support Solicitations and Responses on a Mobile Peer Support Application. Proceedings of the ACM on Human-Computer Interaction 2021;5(CSCW2):1 View
  72. Garg M. Mental Health Analysis in Social Media Posts: A Survey. Archives of Computational Methods in Engineering 2023;30(3):1819 View
  73. 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
  74. García-Martínez C, Oliván-Blázquez B, Fabra J, Martínez-Martínez A, Pérez-Yus M, López-Del-Hoyo Y. Exploring the Risk of Suicide in Real Time on Spanish Twitter: Observational Study. JMIR Public Health and Surveillance 2022;8(5):e31800 View
  75. Chadha A, Kaushik B. A Hybrid Deep Learning Model Using Grid Search and Cross-Validation for Effective Classification and Prediction of Suicidal Ideation from Social Network Data. New Generation Computing 2022;40(4):889 View
  76. Yip P, Pinkney E. Social media and suicide in social movements: a case study in Hong Kong. Journal of Computational Social Science 2022;5(1):1023 View
  77. Patchin J, Hinduja S, Meldrum R. Digital self‐harm and suicidality among adolescents. Child and Adolescent Mental Health 2023;28(1):52 View
  78. 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
  79. Wang Y, Wang Z, Li C, Zhang Y, Wang H. Online social network individual depression detection using a multitask heterogenous modality fusion approach. Information Sciences 2022;609:727 View
  80. Jin H, Nath S, Schneider S, Junghaenel D, Wu S, Kaplan C. An informatics approach to examine decision-making impairments in the daily life of individuals with depression. Journal of Biomedical Informatics 2021;122:103913 View
  81. Liu J, Shi M, Jiang H. Detecting Suicidal Ideation in Social Media: An Ensemble Method Based on Feature Fusion. International Journal of Environmental Research and Public Health 2022;19(13):8197 View
  82. Lyu S, Ren X, Du Y, Zhao N. Detecting depression of Chinese microblog users via text analysis: Combining Linguistic Inquiry Word Count (LIWC) with culture and suicide related lexicons. Frontiers in Psychiatry 2023;14 View
  83. Nti I, Akyeramfo-Sam S, Bediako-Kyeremeh B, Agyemang S. Prediction of social media effects on students’ academic performance using Machine Learning Algorithms (MLAs). Journal of Computers in Education 2022;9(2):195 View
  84. JIANG L, TIAN X, REN P, LUO F. A new type of mental health assessment using artificial intelligence technique. Advances in Psychological Science 2022;30(1):157 View
  85. Mandryk R, Birk M, Vedress S, Wiley K, Reid E, Berger P, Frommel J. Remote Assessment of Depression Using Digital Biomarkers From Cognitive Tasks. Frontiers in Psychology 2021;12 View
  86. Gupta M, Ramar D, Vijayan R, Gupta N. Artificial Intelligence Tools for Suicide Prevention in Adolescents and Young Adults. Adolescent Psychiatry 2022;12(1):1 View
  87. 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
  88. Schick A, Rauschenberg C, Ader L, Daemen M, Wieland L, Paetzold I, Postma M, Schulte-Strathaus J, Reininghaus U. Novel digital methods for gathering intensive time series data in mental health research: scoping review of a rapidly evolving field. Psychological Medicine 2023;53(1):55 View
  89. 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
  90. Pan W, Han Y, Li J, Zhang E, He B. The positive energy of netizens: development and application of fine-grained sentiment lexicon and emotional intensity model. Current Psychology 2023;42(32):27901 View
  91. Kirtley O, van Mens K, Hoogendoorn M, Kapur N, de Beurs D. Translating promise into practice: a review of machine learning in suicide research and prevention. The Lancet Psychiatry 2022;9(3):243 View
  92. Gu Y, Chen D, Liu X. Suicide Possibility Scale Detection via Sina Weibo Analytics: Preliminary Results. International Journal of Environmental Research and Public Health 2022;20(1):466 View
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Books/Policy Documents

  1. Gao J, Cheng Q, Yu P. Proceedings of the Future Technologies Conference (FTC) 2018. View
  2. Kasperiuniene J, Briediene M, Zydziunaite V. Computer Supported Qualitative Research. View
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  4. Eti S, Mızrak F. Strategic Outlook for Innovative Work Behaviours. View
  5. Liao H, Zhou Z, Zhou Y. Intelligent Human Computer Interaction. View
  6. Koltai J, Kmetty Z, Bozsonyi K. Pathways Between Social Science and Computational Social Science. View
  7. Zhu S, Wang X, Liu P. Chinese Lexical Semantics. View
  8. Velupillai S, Davis K, Rozenblit L. Mental Health Informatics. View
  9. Mejova Y. Handbook of Computational Social Science for Policy. View
  10. Ganu L, Arun B. Advanced Machine Intelligence and Signal Processing. View
  11. Thapa S, Ghimire A, Adhikari S, Bhoi A, Barsocchi P. Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data. View
  12. Usharani B, Goyal L. Predictive Analytics of Psychological Disorders in Healthcare. View
  13. Wongkoblap A, Vadillo M, Curcin V. Mental Health in a Digital World. View
  14. Misra P, Yadav A, Chaurasia S. New Opportunities for Sentiment Analysis and Information Processing. View
  15. Guo L, Xia L, Huang X, Fu Y, Li X, Zhou S, Zhao C, Yang B. Health Information Science. View
  16. Orozco-del-Castillo M, Orozco-del-Castillo E, Brito-Borges E, Bermejo-Sabbagh C, Cuevas-Cuevas N. Telematics and Computing. View
  17. Daneshvar H, Boursalie O, Samavi R, Doyle T, Duncan L, Pires P, Sassi R. Artificial Intelligence for Medicine. View