ArticleJMIR mental health2024
Using Large Language Models to Understand Suicidality in a Social Media-Based Taxonomy of Mental Health Disorders: Linguistic Analysis of Reddit Posts.
Article in JMIR mental health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 3 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The Application and Ethical Implication of Generative AI in Mental Health: Systematic Review.JMIR mental health · 2025Pooled it
- Sentiment analysis in public health: a systematic review of the current state, challenges, and future directions.Frontiers in public health · 2025Pooled it
- Exploring the application boundaries of LLMs in mental health: a systematic scoping review.Frontiers in psychology · 2025Pooled it
- Assessing the Need for Mental Health Support From Free-Text Responses: Development and Validation of Language-Based Assessments in Adults With Internalizing Symptoms.JMIR mental health · 2026Article
- Large Language Models for Depression Detection: A Review with Prospects of Incomplete Multimodality.Brain sciences · 2026Review
- Large language models in adolescent suicide prevention: from language signals to accountable action.Frontiers in public health · 2026Review
- Large language models in clinical psychiatry: Applications and optimization strategies.World journal of psychiatry · 2025Review
- Premenstrual dysphoric disorder in online peer support communities: a Reddit case study.Scientific reports · 2025Article
- Current applications and future directions in natural language processing for news media and mental health.Scientific reports · 2025Article
- Supervised Learning and Large Language Model Benchmarks on Mental Health Datasets: Cognitive Distortions and Suicidal Risks in Chinese Social Media.Bioengineering (Basel, Switzerland) · 2025Article
- Systematic Evaluation of Auto-Encoding and Large Language Model Representations for Capturing Author States and Traits.Findings of ACL. ACL · 2025Article
- The Applications of Large Language Models in Mental Health: Scoping Review.Journal of medical Internet research · 2025Article
- Deep Learning-Based Detection of Depression and Suicidal Tendencies in Social Media Data with Feature Selection.Behavioral sciences (Basel, Switzerland) · 2025Article
- Artificial intelligence (AI)-Enabled behavioral health application for college students: Pilot study protocol.PloS one · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Rates of suicide have increased by over 35% since 1999. Despite concerted efforts, our ability to predict, explain, or treat suicide risk has not significantly improved over the past 50 years. Objective: The aim of this study was to use large language models to understand natural language use during public web-based discussions (on Reddit) around topics related to suicidality. Methods: We used large language model-based sentence embedding to extract the latent linguistic dimensions of user postings derived from several mental health-related subreddits, with a focus on suicidality. We then applied dimensionality reduction to these sentence embeddings, allowing them to be summarized and visualized in a lower-dimensional Euclidean space for further downstream analyses. We analyzed 2.9 million posts extracted from 30 subreddits, including r/SuicideWatch, between October 1 and December 31, 2022, and the same period in 2010. Results: Our results showed that, in line with existing theories of suicide, posters in the suicidality community (r/SuicideWatch) predominantly wrote about feelings of disconnection, burdensomeness, hopeless, desperation, resignation, and trauma. Further, we identified distinct latent linguistic dimensions (well-being, seeking support, and severity of distress) among all mental health subreddits, and many of the resulting subreddit clusters were in line with a statistically driven diagnostic classification system-namely, the Hierarchical Taxonomy of Psychopathology (HiTOP)-by mapping onto the proposed superspectra. Conclusions: Overall, our findings provide data-driven support for several language-based theories of suicide, as well as dimensional classification systems for mental health disorders. Ultimately, this novel combination of natural language processing techniques can assist researchers in gaining deeper insights about emotions and experiences shared on the web and may aid in the validation and refutation of different mental health theories.
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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.