Development of Internet suicide message identification and the Monitoring-Tracking-Rescuing model in Taiwan

• Published in: Journal of affective disorders 2023-01, Vol.320, p.37-41
• Main Authors: Wu, En-Liang, Wu, Chia-Yi, Lee, Ming-Been, Chu, Kuo-Chung, Huang, Ming-Shih
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Humans; Internet; Monitoring; Reproducibility of Results; Rescuing; Social Media; Suicidal Ideation; Suicide - psychology; Suicide Prevention; Taiwan; Tracking
• ISSN: 0165-0327; 1573-2517
• DOI: 10.1016/j.jad.2022.09.090

Suicide messages can be transmitted infinitely online; the Internet is influential in suicide prevention. Identifying suicide risks online via artificial technological advances may help predict suicide. We built a classifier that detects open messages containing suicidal ideation or behavior-related words in social media via text mining methods and developed the Monitoring-Tracking-Rescuing model, which links data monitoring and tracking to high-risk suicide rescues. Natural language processing (NLP) techniques such as Long Short-Term Memory and Bidirectional Encoder Representations from Transformers were applied to online posts of common social media sites in Taiwan. This model uses a two-step high-risk identification procedure: an automatic prediction process using NLP to classify suicide-risk levels, followed by professional validation by a senior psychiatrist and a nursing faculty specialized in suicidology. From a dataset containing 404 high-risk and 2226 no- or low-risk articles, the sensitivity and specificity of our model reached 80 %. The model is limited to data platforms that can be “crawled” and excludes suicide-risk content from graphics, video and audio files. Additionally, machine learning does not provide the best recognition rate from complex online messages. Keywords for high-risk suicide in long articles are difficult to interpret using this model. Finally, the model lacks keywords for suicide-protective factors. Artificial intelligence techniques may help detect and monitor high-risk suicide posts and inform mental health professionals of these posts. Periodic tracking plus manual validation to determine risk levels are recommended to enhance the reliability and effectiveness of Internet suicide-prevention tasks. •Traditional prediction models (e.g., logistic regression and linear regression) can include limited risk factors for suicide.•It is difficult to address situations of complex interactions, such as suicide message detection on the internet.•Current mechanisms for suicide identification in social media and rescue rarely rely on automatic radar channels.•Artificial intelligence is potentially developed to detect and determine the degree of suicide risk for early rescue tasks.•The machine-learned classifier represents a significant advancement in detecting suicide-risk messages in social media.•The MTR model proposed in this study is beneficial for the current and future development of internet suicide prevention.