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

Infodemiology and Infoveillance (1479) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1096) Mental Health Surveillance and Epidemiology (81) Intentional Self-Harm (116) Depression and Mood Disorders; Suicide Prevention (1554) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1816) Public (e)Health, Digital Epidemiology and Public Health Informatics (884) Methods and New Tools in Mental Health Research (513)

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 Wang1, 2, 3 Author Orcid Image ;   Huan Ning4 Author Orcid Image ;   Xiao Huang5 Author Orcid Image ;   Yunyu Xiao6 Author Orcid Image ;   Mengxi Zhang7 Author Orcid Image ;   Ellie Fan Yang8 Author Orcid Image ;   Yukio Sadahiro1 Author Orcid Image ;   Yan Liu9 Author Orcid Image ;   Zhenlong Li4 Author Orcid Image ;   Tao Hu10 Author Orcid Image ;   Xiaokang Fu11 Author Orcid Image ;   Zi Li12 Author Orcid Image ;   Ye Zeng13 Author Orcid Image
Article Authors Cited by (10) 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
  3. Li I, Estafanous A, De Choudhury M, Alvarez-Jimenez M, Birnbaum M. Social Media and Early Psychosis Intervention: A Comprehensive Review of the Literature. Current Treatment Options in Psychiatry 2024;11(2):52 View
  4. Wang S, Hu T, Xiao H, Li Y, Zhang C, Ning H, Zhu R, Li Z, Ye X. GPT, large language models (LLMs) and generative artificial intelligence (GAI) models in geospatial science: a systematic review. International Journal of Digital Earth 2024;17(1) View
  5. Holmes G, Tang B, Gupta S, Venkatesh S, Christensen H, Whitton A. Applications of Large Language Models in the Field of Suicide Prevention: Scoping Review. Journal of Medical Internet Research 2025;27:e63126 View
  6. Zhang Y, Cai L, Zhu C. Understanding online suicidal ideation in China: nationwide distribution, social determinants, and geographic variations. Humanities and Social Sciences Communications 2024;11(1) View
  7. Liu X, Susarla A, Padman R. Promoting Health Literacy With Human-in-the-Loop Video Understandability Classification of YouTube Videos: Development and Evaluation Study. Journal of Medical Internet Research 2025;27:e56080 View
  8. Ahmad M, Batyrshin I, Sidorov G. Sentiment Analysis Using a Large Language Model–Based Approach to Detect Opioids Mixed With Other Substances Via Social Media: Method Development and Validation. JMIR Infodemiology 2025;5:e70525 View

Books/Policy Documents

  1. Huang X. The Encyclopedia of Human Geography. View
  2. Cruz R, Magboo M, Magboo V. Computing and Machine Learning. View