ArticleScientific reports2020
Deep neural networks detect suicide risk from textual facebook posts.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 111 citations in OpenAlex.
- Use of Social Media Data to Diagnose and Monitor Psychotic Disorders: Systematic Review.Journal of medical Internet research · 2022Pooled it
- Identifying suicide-related language in smartphone keyboard entries among high-risk adolescents.NPJ digital medicine · 2026Article
- Sentiment trajectory modeling in mental health discussions via transformer-based analysis.Frontiers in artificial intelligence · 2026Article
- Predicting imminent suicide risk in a crisis hotline chat using machine learning.Scientific reports · 2025Article
- [Design and validation of a multimodal model integrating text and imaging data for intelligent assessment of psychological stress in college students].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- Multimodal Multitask Learning for Predicting Depression Severity and Suicide Risk Using Pretrained Audio and Text Embeddings: Methodology Development and Application.JMIR medical informatics · 2025Article
- Large Language Models and Text Embeddings for Detecting Depression and Suicide in Patient Narratives.JAMA network open · 2025Article
- AI and mental health: evaluating supervised machine learning models trained on diagnostic classifications.AI & society · 2025Article
- Machine Learning-Based Suicide Risk Prediction Model for Suicidal Trajectory on Social Media Following Suicidal Mentions: Independent Algorithm Validation.Journal of medical Internet research · 2024Article
- From Data to Hope: Deep Neural Network-Based Prediction of Poisoning (DNNPPS) Suicide Cases.Iranian journal of public health · 2024Article
- Detection of suicidality from medical text using privacy-preserving large language models.The British journal of psychiatry : the journal of mental science · 2024Review
- Automatically extracting social determinants of health for suicide: a narrative literature review.Npj mental health research · 2024Review
- Detecting suicide risk among U.S. servicemembers and veterans: a deep learning approach using social media data.Psychological medicine · 2024Article
- Risk factors and their association network for young adults' suicidality: a cross-sectional study.BMC public health · 2024Article
- A machine learning approach to detect potentially harmful and protective suicide-related content in broadcast media.PloS one · 2024Article
- Bored to death: Artificial Intelligence research reveals the role of boredom in suicide behavior.Frontiers in psychiatry · 2024Article
- A novel multi-model feature generation technique for suicide detection.PeerJ. Computer science · 2024Article
- Diagnosing psychiatric disorders from history of present illness using a large-scale linguistic model.Psychiatry and clinical neurosciences · 2023Article
- Suicide risk detection using artificial intelligence: the promise of creating a benchmark dataset for research on the detection of suicide risk.Frontiers in psychiatry · 2023Article
- A machine learning approach to identifying suicide risk among text-based crisis counseling encounters.Frontiers in psychiatry · 2023Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Detection of suicide risk is a highly prioritized, yet complicated task. Five decades of research have produced predictions slightly better than chance (AUCs = 0.56-0.58). In this study, Artificial Neural Network (ANN) models were constructed to predict suicide risk from everyday language of social media users. The dataset included 83,292 postings authored by 1002 authenticated Facebook users, alongside valid psychosocial information about the users. Using Deep Contextualized Word Embeddings for text representation, two models were constructed: A Single Task Model (STM), to predict suicide risk from Facebook postings directly (Facebook texts → suicide) and a Multi-Task Model (MTM), which included hierarchical, multilayered sets of theory-driven risk factors (Facebook texts → personality traits → psychosocial risks → psychiatric disorders → suicide). Compared with the STM predictions (0.621 ≤ AUC ≤ 0.629), the MTM produced significantly improved prediction accuracy (0.697 ≤ AUC ≤ 0.746), with substantially larger effect sizes (0.729 ≤ d ≤ 0.936). Subsequent content analyses suggested that predictions did not rely on explicit suicide-related themes, but on a range of text features. The findings suggest that machine learning based analyses of everyday social media activity can improve suicide risk predictions and contribute to the development of practical detection tools.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.