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

Peer-to-Peer Support and Online Communities (463) Depression and Mood Disorders; Suicide Prevention (1029) Behavioural Surveillance for Public Health (117) Statistical Methods for Surveillance and Population Health (112)

Published on in Vol 20 , No 6 (2018) :June

Detecting Suicidal Ideation on Forums: Proof-of-Concept Study

Detecting Suicidal Ideation on Forums: Proof-of-Concept Study

Detecting Suicidal Ideation on Forums: Proof-of-Concept Study

Authors of this article:

Ahmet Emre Aladağ 1, 2 Author Orcid Image ;   Serra Muderrisoglu 3 Author Orcid Image ;   Naz Berfu Akbas 4 Author Orcid Image ;   Oguzhan Zahmacioglu 5 Author Orcid Image ;   Haluk O. Bingol 1 Author Orcid Image
Article Authors Cited by (59) Tweetations (8) Metrics

Journals

  1. Oexle N, Niederkrotenthaler T, DeLeo D. Emerging trends in suicide prevention research. Current Opinion in Psychiatry 2019;32(4):336 View
  2. 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
  3. Zunic A, Corcoran P, Spasic I. Sentiment Analysis in Health and Well-Being: Systematic Review. JMIR Medical Informatics 2020;8(1):e16023 View
  4. Cacheda F, Fernandez D, Novoa F, Carneiro V. Early Detection of Depression: Social Network Analysis and Random Forest Techniques. Journal of Medical Internet Research 2019;21(6):e12554 View
  5. 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
  6. 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
  7. Shen Y, Zhang W, Chan B, Zhang Y, Meng F, Kennon E, Wu H, Luo X, Zhang X. Detecting risk of suicide attempts among Chinese medical college students using a machine learning algorithm. Journal of Affective Disorders 2020;273:18 View
  8. Strand M, Gustafsson S. Mukbang and Disordered Eating: A Netnographic Analysis of Online Eating Broadcasts. Culture, Medicine, and Psychiatry 2020;44(4):586 View
  9. Soron T, Shariful Islam S. Suicide on Facebook-the tales of unnoticed departure in Bangladesh. Global Mental Health 2020;7 View
  10. Davis , Sedig , Lizotte . Archetype-Based Modeling and Search of Social Media. Big Data and Cognitive Computing 2019;3(3):44 View
  11. 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
  12. Yao H, Rashidian S, Dong X, Duanmu H, Rosenthal R, Wang F. Detection of Suicidality Among Opioid Users on Reddit: Machine Learning–Based Approach. Journal of Medical Internet Research 2020;22(11):e15293 View
  13. 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
  14. Garg S, Raigosa A, Aiman R. Investigating differential linguistic patterns exhibited by Major Depressive Disorder (MDD) Patients and building a Long Short Term Memory Network + Convolutional Neural Network Model, Logistic Regression model, and a Multinomial Naive Bayes Classifier Algorithm to develop Spero, a hybrid app based Early-MDD diagnosis system. International Journal of Scientific Research in Computer Science, Engineering and Information Technology 2020:114 View
  15. Forte A, Sarli G, Polidori L, Lester D, Pompili M. The Role of New Technologies to Prevent Suicide in Adolescence: A Systematic Review of the Literature. Medicina 2021;57(2):109 View
  16. Skaik R, Inkpen D. Using Social Media for Mental Health Surveillance. ACM Computing Surveys 2021;53(6):1 View
  17. 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
  18. Mason A, Jang K, Morley K, Scarf D, Collings S, Riordan B. A Content Analysis of Reddit Users' Perspectives on Reasons for Not Following Through with a Suicide Attempt. Cyberpsychology, Behavior, and Social Networking 2021;24(10):642 View
  19. López-Úbeda P, Plaza-del-Arco F, Díaz-Galiano M, Martín-Valdivia M. How Successful Is Transfer Learning for Detecting Anorexia on Social Media?. Applied Sciences 2021;11(4):1838 View
  20. Cheng Q, Lui C. Applying text mining methods to suicide research. Suicide and Life-Threatening Behavior 2021;51(1):137 View
  21. S S, S. Raj J. Analysis of Deep Learning Techniques for Early Detection of Depression on Social Media Network - A Comparative Study. Journal of Trends in Computer Science and Smart Technology 2021;3(1):24 View
  22. Lekkas D, Klein R, Jacobson N. Predicting acute suicidal ideation on Instagram using ensemble machine learning models. Internet Interventions 2021;25:100424 View
  23. Arillotta D, Guirguis A, Corkery J, Scherbaum N, Schifano F. COVID-19 Pandemic Impact on Substance Misuse: A Social Media Listening, Mixed Method Analysis. Brain Sciences 2021;11(7):907 View
  24. Silveira B, Silva H, Murai F, da Silva A. Predicting user emotional tone in mental disorder online communities. Future Generation Computer Systems 2021;125:641 View
  25. 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
  26. Haque R, Islam N, Islam M, Ahsan M. A Comparative Analysis on Suicidal Ideation Detection Using NLP, Machine, and Deep Learning. Technologies 2022;10(3):57 View
  27. 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
  28. Ungless E, Ross B, Belle V. Potential Pitfalls With Automatic Sentiment Analysis: The Example of Queerphobic Bias. Social Science Computer Review 2023:089443932311529 View
  29. Fukazawa Y. Estimating Mental Health Using Human-generated Big Data and Machine Learning. The Brain & Neural Networks 2022;29(2):78 View
  30. Garg M. Mental Health Analysis in Social Media Posts: A Survey. Archives of Computational Methods in Engineering 2023;30(3):1819 View
  31. Tejaswini V, Babu K, Sahoo B. Depression Detection from Social Media Text Analysis using Natural Language Processing Techniques and Hybrid Deep Learning Model. ACM Transactions on Asian and Low-Resource Language Information Processing 2022 View
  32. 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
  33. Chancellor S, Sumner S, David-Ferdon C, Ahmad T, De Choudhury M. Suicide Risk and Protective Factors in Online Support Forum Posts: Annotation Scheme Development and Validation Study. JMIR Mental Health 2021;8(11):e24471 View
  34. Huang Y, Zhu C, Feng Y, Ji Y, Song J, Wang K, Yu F. Comparison of three machine learning models to predict suicidal ideation and depression among Chinese adolescents: A cross-sectional study. Journal of Affective Disorders 2022;319:221 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. 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
  37. Renjith S, Abraham A, Jyothi S, Chandran L, Thomson J. An ensemble deep learning technique for detecting suicidal ideation from posts in social media platforms. Journal of King Saud University - Computer and Information Sciences 2022;34(10):9564 View
  38. Kodati D, Tene R. Identifying suicidal emotions on social media through transformer-based deep learning. Applied Intelligence 2022 View
  39. Cao X, Liu X. Artificial intelligence-assisted psychosis risk screening in adolescents: Practices and challenges. World Journal of Psychiatry 2022;12(10):1287 View
  40. 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
  41. Zhang T, Yang K, Ji S, Ananiadou S. Emotion fusion for mental illness detection from social media: A survey. Information Fusion 2023;92:231 View
  42. 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
  43. Ptaszynski M, Zasko-Zielinska M, Marcinczuk M, Leliwa G, Fortuna M, Soliwoda K, Dziublewska I, Hubert O, Skrzek P, Piesiewicz J, Karbowska P, Dowgiallo M, Eronen J, Tempska P, Brochocki M, Godny M, Wroczynski M. Looking for Razors and Needles in a Haystack: Multifaceted Analysis of Suicidal Declarations on Social Media—A Pragmalinguistic Approach. International Journal of Environmental Research and Public Health 2021;18(22):11759 View
  44. Gupta D, Markale A, Kulkarni R. Mental Health Quantifier. International Journal of Engineering and Advanced Technology 2021;10(5):187 View
  45. 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
  46. Smrke U, Mlakar I, Lin S, Musil B, Plohl N. Language, Speech, and Facial Expression Features for Artificial Intelligence–Based Detection of Cancer Survivors’ Depression: Scoping Meta-Review. JMIR Mental Health 2021;8(12):e30439 View
  47. 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
  48. Amanat A, Rizwan M, Javed A, Abdelhaq M, Alsaqour R, Pandya S, Uddin M. Deep Learning for Depression Detection from Textual Data. Electronics 2022;11(5):676 View
  49. Sami Khafaga D, Auvdaiappan M, Deepa K, Abouhawwash M, Khalid Karim F. Deep Learning for Depression Detection Using Twitter Data. Intelligent Automation & Soft Computing 2023;36(2):1301 View

Books/Policy Documents

  1. Andavar V, Gupta S. Bio-Inspired Algorithms and Devices for Treatment of Cognitive Diseases Using Future Technologies. View
  2. Mantilla-Saavedra C, Gutiérrez-Cárdenas J. Information Management and Big Data. View
  3. Madkar S, Maheshwari T, Merani M, Merchant R, Doshi P. Advances in Computing and Data Sciences. View
  4. Jyothi S, Renjith S. Advances in Data and Information Sciences. View