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

Infodemiology and Infoveillance (1166) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (946) Mental Health Surveillance and Epidemiology (36) Intentional Self-Harm (72) Depression and Mood Disorders; Suicide Prevention (1169) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1449) Public (e)Health, Digital Epidemiology and Public Health Informatics (486) Methods and New Tools in Mental Health Research (396)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/47225, first published .
Public Surveillance of Social Media for Suicide Using Advanced Deep Learning Models in Japan: Time Series Study From 2012 to 2022

Public Surveillance of Social Media for Suicide Using Advanced Deep Learning Models in Japan: Time Series Study From 2012 to 2022

Public Surveillance of Social Media for Suicide Using Advanced Deep Learning Models in Japan: Time Series Study From 2012 to 2022

Authors of this article:

Siqin Wang 1, 2, 3 Author Orcid Image ;   Huan Ning 4 Author Orcid Image ;   Xiao Huang 5 Author Orcid Image ;   Yunyu Xiao 6 Author Orcid Image ;   Mengxi Zhang 7 Author Orcid Image ;   Ellie Fan Yang 8 Author Orcid Image ;   Yukio Sadahiro 1 Author Orcid Image ;   Yan Liu 9 Author Orcid Image ;   Zhenlong Li 4 Author Orcid Image ;   Tao Hu 10 Author Orcid Image ;   Xiaokang Fu 11 Author Orcid Image ;   Zi Li 12 Author Orcid Image ;   Ye Zeng 13 Author Orcid Image
Article Authors Cited by (2) Tweetations (7) Metrics

Journals

  1. Carabot F, Fraile-Martínez O, Donat-Vargas C, Santoma J, Garcia-Montero C, Pinto da Costa M, Molina-Ruiz R, Ortega M, Alvarez-Mon M, Alvarez-Mon M. Understanding Public Perceptions and Discussions on Opioids Through Twitter: Cross-Sectional Infodemiology Study. Journal of Medical Internet Research 2023;25:e50013 View
  2. Bello H, Palomar-Ciria N, Lozano C, Gutiérrez-Alonso C, Baca-García E. Examining the relationship between COVID-19 and suicide in media coverage through Natural Language Processing analysis. The European Journal of Psychiatry 2024;38(1):100227 View