This paper is in the following e-collection/theme issue:

Depression and Mood Disorders; Suicide Prevention (1290) Infodemiology and Infoveillance (1278) Natural Language Processing (649) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1014)

Published on in Vol 3, No 2 (2016): Apr-Jun

Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality

Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality

Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality

Authors of this article:

Scott R. Braithwaite1 Author Orcid Image ;   Christophe Giraud-Carrier2 Author Orcid Image ;   Josh West3 Author Orcid Image ;   Michael D. Barnes3 Author Orcid Image ;   Carl Lee Hanson3 Author Orcid Image
Article Authors Cited by (127) Tweetations (49) Metrics

Journals

  1. 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
  2. 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
  3. Carreiro S, Chai P, Carey J, Chapman B, Boyer E. Integrating Personalized Technology in Toxicology: Sensors, Smart Glass, and Social Media Applications in Toxicology Research. Journal of Medical Toxicology 2017;13(2):166 View
  4. Paul M, Dredze M. Social Monitoring for Public Health. Synthesis Lectures on Information Concepts, Retrieval, and Services 2017;9(5):1 View
  5. 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
  6. Soucy J, Hadjistavropoulos H, Couture C, Owens V, Dear B, Titov N. Content of client emails in internet-delivered cognitive behaviour therapy: A comparison between two trials and relationship to client outcome. Internet Interventions 2018;11:53 View
  7. Grant R, Kucher D, León A, Gemmell J, Raicu D, Fodeh S. Automatic extraction of informal topics from online suicidal ideation. BMC Bioinformatics 2018;19(S8) View
  8. 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
  9. 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
  10. Colditz J, Chu K, Emery S, Larkin C, James A, Welling J, Primack B. Toward Real-Time Infoveillance of Twitter Health Messages. American Journal of Public Health 2018;108(8):1009 View
  11. Roy A, Nikolitch K, McGinn R, Jinah S, Klement W, Kaminsky Z. A machine learning approach predicts future risk to suicidal ideation from social media data. npj Digital Medicine 2020;3(1) View
  12. 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
  13. Glenn J, Nobles A, Barnes L, Teachman B. Can Text Messages Identify Suicide Risk in Real Time? A Within-Subjects Pilot Examination of Temporally Sensitive Markers of Suicide Risk. Clinical Psychological Science 2020;8(4):704 View
  14. Spates K, Ye X, Johnson A. “I just might kill myself”: Suicide expressions on Twitter. Death Studies 2020;44(3):189 View
  15. Cohan A, Young S, Yates A, Goharian N. Triaging content severity in online mental health forums. Journal of the Association for Information Science and Technology 2017;68(11):2675 View
  16. 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
  17. Walters K, Christakis D, Wright D, Alamian A. Are Mechanical Turk worker samples representative of health status and health behaviors in the U.S.?. PLOS ONE 2018;13(6):e0198835 View
  18. Oexle N, Niederkrotenthaler T, DeLeo D. Emerging trends in suicide prevention research. Current Opinion in Psychiatry 2019;32(4):336 View
  19. 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
  20. Priya A, Garg S, Tigga N. Predicting Anxiety, Depression and Stress in Modern Life using Machine Learning Algorithms. Procedia Computer Science 2020;167:1258 View
  21. Brown R, Bendig E, Fischer T, Goldwich A, Baumeister H, Plener P, Harris K. Can acute suicidality be predicted by Instagram data? Results from qualitative and quantitative language analyses. PLOS ONE 2019;14(9):e0220623 View
  22. 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
  23. Tadesse M, Lin H, Xu B, Yang L. Detection of Suicide Ideation in Social Media Forums Using Deep Learning. Algorithms 2019;13(1):7 View
  24. Barnes M, Hanson C, Giraud-Carrier C. The Case for Computational Health Science. Journal of Healthcare Informatics Research 2018;2(1-2):99 View
  25. 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
  26. Brent D. Commentary: A time to reap and a time to sow: reducing the adolescent suicide rate now and in the future: commentary on Cha et al. (2018). Journal of Child Psychology and Psychiatry 2018;59(4):483 View
  27. 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
  28. 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
  29. Niederkrotenthaler T, Till B, Garcia D. Celebrity suicide on Twitter: Activity, content and network analysis related to the death of Swedish DJ Tim Bergling alias Avicii. Journal of Affective Disorders 2019;245:848 View
  30. King C, Arango A, Ewell Foster C. Emerging trends in adolescent suicide prevention research. Current Opinion in Psychology 2018;22:89 View
  31. Mavragani A. Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206 View
  32. Burnap P, Colombo G, Amery R, Hodorog A, Scourfield J. Multi-class machine classification of suicide-related communication on Twitter. Online Social Networks and Media 2017;2:32 View
  33. Chiang C, Kasunic A, Savage S. Crowd Coach. Proceedings of the ACM on Human-Computer Interaction 2018;2(CSCW):1 View
  34. Kornfield R, Sarma P, Shah D, McTavish F, Landucci G, Pe-Romashko K, Gustafson D. Detecting Recovery Problems Just in Time: Application of Automated Linguistic Analysis and Supervised Machine Learning to an Online Substance Abuse Forum. Journal of Medical Internet Research 2018;20(6):e10136 View
  35. Melia R, Francis K, Duggan J, Bogue J, O'Sullivan M, Chambers D, Young K. Mobile Health Technology Interventions for Suicide Prevention: Protocol for a Systematic Review and Meta-Analysis. JMIR Research Protocols 2018;7(1):e28 View
  36. 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
  37. Sabus C, Johns B, Schultz N, Gagnon K. Exploration of Content and Reach of Physical Therapy-Related Discussion on Twitter. Physical Therapy 2019;99(8):1048 View
  38. Skaik R, Inkpen D. Using Social Media for Mental Health Surveillance. ACM Computing Surveys 2021;53(6):1 View
  39. Kruzan K, Whitlock J, Bazarova N. Examining the Relationship Between the Use of a Mobile Peer-Support App and Self-Injury Outcomes: Longitudinal Mixed Methods Study. JMIR Mental Health 2021;8(1):e21854 View
  40. Weintraub M, Posta F, Arevian A, Miklowitz D. Using machine learning analyses of speech to classify levels of expressed emotion in parents of youth with mood disorders. Journal of Psychiatric Research 2021;136:39 View
  41. 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
  42. Tiwari P, Sharma M, Garg P, Jain T, Verma V, Hussain A. A Study on Sentiment Analysis of Mental Illness Using Machine Learning Techniques. IOP Conference Series: Materials Science and Engineering 2021;1099(1):012043 View
  43. 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
  44. Lossio-Ventura J, Gonzales S, Morzan J, Alatrista-Salas H, Hernandez-Boussard T, Bian J. Evaluation of clustering and topic modeling methods over health-related tweets and emails. Artificial Intelligence in Medicine 2021;117:102096 View
  45. Lekkas D, Klein R, Jacobson N. Predicting acute suicidal ideation on Instagram using ensemble machine learning models. Internet Interventions 2021;25:100424 View
  46. Gillan C, Rutledge R. Smartphones and the Neuroscience of Mental Health. Annual Review of Neuroscience 2021;44(1):129 View
  47. Yang T, Li F, Ji D, Liang X, Xie T, Tian S, Li B, Liang P. Fine-grained depression analysis based on Chinese micro-blog reviews. Information Processing & Management 2021;58(6):102681 View
  48. 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
  49. 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
  50. 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
  51. Aldhyani T, Alsubari S, Alshebami A, Alkahtani H, Ahmed Z. Detecting and Analyzing Suicidal Ideation on Social Media Using Deep Learning and Machine Learning Models. International Journal of Environmental Research and Public Health 2022;19(19):12635 View
  52. 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
  53. 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
  54. Khazanov G, Forbes C, Dunn B, Thase M. Addressing anhedonia to increase depression treatment engagement. British Journal of Clinical Psychology 2022;61(2):255 View
  55. Adarsh V, Arun Kumar P, Lavanya V, Gangadharan G. Fair and Explainable Depression Detection in Social Media. Information Processing & Management 2023;60(1):103168 View
  56. Mishra S, Tripathy H, Kumar Thakkar H, Garg D, Kotecha K, Pandya S. An Explainable Intelligence Driven Query Prioritization Using Balanced Decision Tree Approach for Multi-Level Psychological Disorders Assessment. Frontiers in Public Health 2021;9 View
  57. 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
  58. Moon N, Mariam A, Sharmin S, Islam M, Nur F, Debnath N. Machine learning approach to predict the depression in job sectors in Bangladesh. Current Research in Behavioral Sciences 2021;2:100058 View
  59. 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
  60. Yeskuatov E, Chua S, Foo L. Leveraging Reddit for Suicidal Ideation Detection: A Review of Machine Learning and Natural Language Processing Techniques. International Journal of Environmental Research and Public Health 2022;19(16):10347 View
  61. Kmetty Z, Bozsonyi K. Identifying Depression-Related Behavior on Facebook—An Experimental Study. Social Sciences 2022;11(3):135 View
  62. Stupinski A, Alshaabi T, Arnold M, Adams J, Minot J, Price M, Dodds P, Danforth C. Quantifying Changes in the Language Used Around Mental Health on Twitter Over 10 Years: Observational Study. JMIR Mental Health 2022;9(3):e33685 View
  63. Kouter K, Videtic Paska A. ‘Omics’ of suicidal behaviour: A path to personalised psychiatry. World Journal of Psychiatry 2021;11(10):774 View
  64. Islam J, Akhand M, Habib M, Kamal M, Siddique N. Recognition of Emotion from Emoticon with Text in Microblog Using LSTM. Advances in Science, Technology and Engineering Systems Journal 2021;6(3):347 View
  65. Acuña Caicedo R, Gómez Soriano J, Melgar Sasieta H. Bootstrapping semi-supervised annotation method for potential suicidal messages. Internet Interventions 2022;28:100519 View
  66. 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
  67. Lomotey R, Kumi S, Hilton M, Orji R, Deters R. Using Machine Learning to Establish the Concerns of Persons With HIV/AIDS During the COVID-19 Pandemic From Their Tweets. IEEE Access 2023;11:37570 View
  68. Di Cara N, Maggio V, Davis O, Haworth C. Methodologies for Monitoring Mental Health on Twitter: Systematic Review. Journal of Medical Internet Research 2023;25:e42734 View
  69. Hilty D, Stubbe D, McKean A, Hoffman P, Zalpuri I, Myint M, Joshi S, Pakyurek M, Li S. A scoping review of social media in child, adolescents and young adults: research findings in depression, anxiety and other clinical challenges. BJPsych Open 2023;9(5) View
  70. Hasib K, Islam M, Sakib S, Akbar M, Razzak I, Alam M. Depression Detection From Social Networks Data Based on Machine Learning and Deep Learning Techniques: An Interrogative Survey. IEEE Transactions on Computational Social Systems 2023;10(4):1568 View
  71. 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
  72. Zhao Y, Liu D, Wan C, Liu X, Nie J, Liu J. JMS-QA: A Joint Hierarchical Architecture for Mental Health Question Answering. IEEE/ACM Transactions on Audio, Speech, and Language Processing 2024;32:352 View
  73. Liu S, Sloan L, Al Baghal T, Williams M, Jessop C, Serôdio P. Linking survey with Twitter data: examining associations among smartphone usage, privacy concern and Twitter linkage consent. International Journal of Social Research Methodology 2024:1 View
  74. Li X, Chen F, Ma L. Exploring the Potential of Artificial Intelligence in Adolescent Suicide Prevention: Current Applications, Challenges, and Future Directions. Psychiatry 2024;87(1):7 View
  75. Patel D, Sumner S, Bowen D, Zwald M, Yard E, Wang J, Law R, Holland K, Nguyen T, Mower G, Chen Y, Johnson J, Jespersen M, Mytty E, Lee J, Bauer M, Caine E, De Choudhury M. Predicting state level suicide fatalities in the united states with realtime data and machine learning. npj Mental Health Research 2024;3(1) View
  76. Kaur I, Kamini , Kaur J, Gagandeep , Singh S, Gupta U. Enhancing explainability in predicting mental health disorders using human–machine interaction. Multimedia Tools and Applications 2024 View

Books/Policy Documents

  1. Lawrence D, Carrington-Jones P, Kyron M. Alternatives to Suicide. View
  2. Liang C, Abbott D, Hong Y, Madadi M, White A. Social Computing and Social Media. Design, Human Behavior and Analytics. View
  3. Roza T, Patusco L, Zimerman A, Ballester P, Passos I. Precision Medicine for Investigators, Practitioners and Providers. View
  4. Adrian M, Lyon A. Technology and Adolescent Mental Health. View
  5. Kessler R, Bernecker S, Bossarte R, Luedtke A, McCarthy J, Nock M, Pigeon W, Petukhova M, Sadikova E, VanderWeele T, Zuromski K, Zaslavsky A. Personalized Psychiatry. View
  6. Ebert D, Harrer M, Apolinário-Hagen J, Baumeister H. Frontiers in Psychiatry. View
  7. Kamarudin N, Beigi G, Manikonda L, Liu H. Open Source Intelligence and Cyber Crime. View
  8. Koltai J, Kmetty Z, Bozsonyi K. Pathways Between Social Science and Computational Social Science. View
  9. Sakiyama K, de Souza Rodrigues L, Matsubara E. Intelligent Systems. View
  10. Chanda K, Ghosh A, Dey S, Bose R, Roy S. Smart IoT for Research and Industry. View
  11. Henry S, Yetisgen M, Uzuner O. Mental Health Informatics. View
  12. Trifan A, Salgado P, Ribeiro J, Oliveira J. Early Detection of Mental Health Disorders by Social Media Monitoring. View
  13. Galán-Madruga D, del Carmen González-Caballero M, Tarazona J. Encyclopedia of Toxicology. View
  14. Pramanik R, Khare S, Harshvardhan G, Gourisaria M. Advances in Data and Information Sciences. View
  15. Babulal K, Nayak B. The Internet of Medical Things (IoMT) and Telemedicine Frameworks and Applications. View
  16. Thapa S, Ghimire A, Adhikari S, Bhoi A, Barsocchi P. Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data. View
  17. Wongkoblap A, Vadillo M, Curcin V. Mental Health in a Digital World. View
  18. Chen X, Genc Y. Artificial Intelligence in HCI. View
  19. Bisht S, Shandilya H, Gupta V, Agrawal S, Jain S. Artificial Intelligence, Machine Learning, and Mental Health in Pandemics. View
  20. Safa R, Edalatpanah S, Sorourkhah A. Deep Learning in Personalized Healthcare and Decision Support. View
  21. Jayapal C, Yamuna S, Manavallan S, Devasenan M. Communication and Intelligent Systems. View
  22. Tahsin M, Jasim S, Naheen I. Inventive Communication and Computational Technologies. View
  23. Whig P, Velu A, Nadikattu R, Alkali Y. Handbook of Computational Sciences. View
  24. Liang Z, Liu D, Wan Q, Liu X, Liao G, Wan C. Social Media Processing. View
  25. Wang Z, Jin M, Lu Y. Cognitive Computing – ICCC 2023. View
  26. Vijaya Sri D, Sai A, Anand V, Manjusha K. Proceedings of Data Analytics and Management. View
  27. Zhu W, Zhang Y, Yu X, Lu M, Lin H. Health Information Processing. View
  28. Mehta U, Bagali K, Kommanapalli S. AI, Consciousness and The New Humanism. View