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

Early Results in Infodemiology and Infoveillance (69) Artificial Intelligence (1328) Natural Language Processing (677) Depression and Mood Disorders; Suicide Prevention (1341) Mass Media/Social Media Communication and Campaigns (458) Infodemiology and Infoveillance (1333) Linguistics, Comprehension, and Learning (52)

Published on in Vol 6, No 8 (2022): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/35563, first published .
Analyzing Suicide Risk From Linguistic Features in Social Media: Evaluation Study

Analyzing Suicide Risk From Linguistic Features in Social Media: Evaluation Study

Analyzing Suicide Risk From Linguistic Features in Social Media: Evaluation Study

Authors of this article:

Cecilia Lao1 Author Orcid Image ;   Jo Lane2 Author Orcid Image ;   Hanna Suominen1, 3 Author Orcid Image
Article Authors Cited by (7) Tweetations (6) Metrics

Journals

  1. Fu J, Li C, Zhou C, Li W, Lai J, Deng S, Zhang Y, Guo Z, Wu Y. Methods for Analyzing the Contents of Social Media for Health Care: Scoping Review. Journal of Medical Internet Research 2023;25:e43349 View
  2. Pei Y, O'Brien K. Use of Social Media Data Mining to Examine Needs, Concerns, and Experiences of People With Traumatic Brain Injury. American Journal of Speech-Language Pathology 2024;33(2):831 View
  3. Khan A, Ali R. Unraveling minds in the digital era: a review on mapping mental health disorders through machine learning techniques using online social media. Social Network Analysis and Mining 2024;14(1) View
  4. 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

Books/Policy Documents

  1. Sazzed S. Social, Cultural, and Behavioral Modeling. View