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

Hannah Metzler   1, 2, 3, 4, 5 , Mag, PhD ;   Hubert Baginski   3, 6 , BSc, MSc ;   Thomas Niederkrotenthaler   2 , MBA, Dr med, PhD ;   David Garcia   1, 3, 4 , MSc, PhD

1 Section for the Science of Complex Systems, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria

2 Unit Suicide Research and Mental Health Promotion, Center for Public Health, Medical University of Vienna, Vienna, Austria

3 Complexity Science Hub Vienna, Vienna, Austria

4 Computational Social Science Lab, Institute of Interactive Systems and Data Science, Graz University of Technology, Graz, Austria

5 Institute for Globally Distributed Open Research and Education, Vienna, Austria

6 Institute of Information Systems Engineering, Vienna University of Technology, Vienna, Austria

Corresponding Author:

  • Hannah Metzler, Mag, PhD
  • Section for the Science of Complex Systems
  • Center for Medical Statistics, Informatics and Intelligent Systems
  • Medical University of Vienna
  • Spitalgasse 23
  • Vienna, 1090
  • Austria
  • Phone: 43 159991 ext 604
  • Email: metzler@csh.ac.at