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

Peer-to-Peer Support and Online Communities (407) Depression and Mood Disorders; Suicide Prevention (890) Behavioural Surveillance for Public Health (99) Statistical Methods for Surveillance and Population Health (94)

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 (27) 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 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