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
All Time | Last Month | Last Six Months | Last Year
There are currently no tweets available for this article within the specified range.