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

e-Mental Health and Cyberpsychology (1388) Natural Language Processing (637) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1553) Machine Learning (1373) Infodemiology and Infoveillance (1270) Diagnostic Tools in Mental Health (220)

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 (10) 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 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