Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications

Abstract

Suicide is a critical issue in modern society. Early detection and prevention of suicide attempts should be addressed to save people's life. Current suicidal ideation detection methods include clinical methods based on the interaction between social workers or experts and the targeted individuals and machine learning techniques with feature engineering or deep learning for automatic detection based on online social contents. This paper is the first survey that comprehensively introduces and discusses the methods from these categories. Domain-specific applications of suicidal ideation detection are reviewed according to their data sources, i.e., questionnaires, electronic health records, suicide notes, and online user content. Several specific tasks and datasets are introduced and summarized to facilitate further research. Finally, we summarize the limitations of current work and provide an outlook of further research directions.

arXiv commentIEEE Transactions on Computational Social Systems

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Cited by in corpus (9)

• Suicidal Ideation and Mental Disorder Detection with Attentive Relation Networks
• Characterization of Time-variant and Time-invariant Assessment of Suicidality on Reddit using C-SSRS
• BiERU: Bidirectional Emotional Recurrent Unit for Conversational Sentiment Analysis
• Artificial Intelligence for Suicide Assessment using Audiovisual Cues: A Review
• Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling
• Building and Using Personal Knowledge Graph to Improve Suicidal Ideation Detection on Social Media
• Data Quality Matters: Suicide Intention Detection on Social Media Posts Using RoBERTa-CNN
• Social Behavior and Mental Health: A Snapshot Survey under COVID-19 Pandemic
• Learning Models for Suicide Prediction from Social Media Posts