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

Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1512) Natural Language Processing (688) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1639) Machine Learning (1458) Infodemiology and Infoveillance (1349) Diagnostic Tools in Mental Health (241)

Published on in Vol 24, No 8 (2022): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34705, first published .
Detecting Potentially Harmful and Protective Suicide-Related Content on Twitter: Machine Learning Approach

Detecting Potentially Harmful and Protective Suicide-Related Content on Twitter: Machine Learning Approach

Detecting Potentially Harmful and Protective Suicide-Related Content on Twitter: Machine Learning Approach

Authors of this article:

Hannah Metzler1, 2, 3, 4, 5 Author Orcid Image ;   Hubert Baginski3, 6 Author Orcid Image ;   Thomas Niederkrotenthaler2 Author Orcid Image ;   David Garcia1, 3, 4 Author Orcid Image
Article Authors Cited by (17) Tweetations (13) Metrics

Journals

  1. Jamil M, Pais S, Cordeiro J. Detection of dangerous events on social media: a critical review. Social Network Analysis and Mining 2022;12(1) View
  2. Di Natale A, Garcia D. LEXpander: Applying colexification networks to automated lexicon expansion. Behavior Research Methods 2023;56(2):952 View
  3. Santoso M, Suryadi J, Marchellino K, Nabiilah G, Rojali . A Comparative Analysis of Decision Tree and Support Vector Machine on Suicide Ideation Detection. Procedia Computer Science 2023;227:518 View
  4. Abdulsalam A, Alhothali A, Al-Ghamdi S. Detecting Suicidality in Arabic Tweets Using Machine Learning and Deep Learning Techniques. Arabian Journal for Science and Engineering 2024;49(9):12729 View
  5. Montejo-Ráez A, Molina-González M, Jiménez-Zafra S, García-Cumbreras M, García-López L. A survey on detecting mental disorders with natural language processing: Literature review, trends and challenges. Computer Science Review 2024;53:100654 View
  6. Mirtaheri S, Greco S, Shahbazian R. A self-attention TCN-based model for suicidal ideation detection from social media posts. Expert Systems with Applications 2024;255:124855 View
  7. Atmakuru A, Shahini A, Chakraborty S, Seoni S, Salvi M, Hafeez-Baig A, Rashid S, Tan R, Barua P, Molinari F, Acharya U. Artificial intelligence-based suicide prevention and prediction: A systematic review (2019–2023). Information Fusion 2025;114:102673 View
  8. Benjachairat P, Senivongse T, Taephant N, Puvapaisankit J, Maturosjamnan C, Kultananawat T. Classification of suicidal ideation severity from Twitter messages using machine learning. International Journal of Information Management Data Insights 2024;4(2):100280 View
  9. Abdulsalam A, Alhothali A. Suicidal ideation detection on social media: a review of machine learning methods. Social Network Analysis and Mining 2024;14(1) View
  10. Guo Z, Lai A, Thygesen J, Farrington J, Keen T, Li K. Large Language Models for Mental Health Applications: Systematic Review. JMIR Mental Health 2024;11:e57400 View