--- title: "Ground Truths in Suicide Research — paper by Ophir et al. (2026)" description: "AI models that flag suicide risk from social media often report strong results. 1 reference. Among them: 0." authors: ["Yaakov Ophir", "Ofri Hefetz", "Refael Tikochinski", "Kfir Bar", "Shir Lissak", "Shulamit Grinapol", "Haya Wachtel", "Eyal Fruchter", "Roi Reichart"] year: 2026 venue: "Proceedings of the 11th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2026)" source: "https://aclanthology.org/2026.clpsych-1.19/" doi: "10.18653/v1/2026.clpsych-1.19" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/ophir-2026-ground-truths-suicide-research-current/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/ophir-2026-ground-truths-suicide-research-current/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/35/) / Ground Truths in Suicide Research: The Current State of AI-Based Suicide Detection in Social Media # Ground Truths in Suicide Research: The Current State of AI-Based Suicide Detection in Social Media **Authors:** Yaakov Ophir, [Ofri Hefetz](https://md.research.elamur.ai/free/en/authors/ofri-hefetz/), Refael Tikochinski, [Kfir Bar](https://md.research.elamur.ai/free/en/authors/kfir-bar/), Shir Lissak, Shulamit Grinapol, Haya Wachtel, Eyal Fruchter and [Roi Reichart](https://md.research.elamur.ai/free/en/authors/roi-reichart/) · **Year:** 2026 · **Venue:** [Proceedings of the 11th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2026)](https://md.research.elamur.ai/free/en/venues/clpsych/) ([Workshop on Computational Linguistics and Clinical Psychology 2026](https://md.research.elamur.ai/free/en/venues/clpsych/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2026.clpsych-1.19/) · [PDF](https://aclanthology.org/2026.clpsych-1.19.pdf) · [DOI 10.18653/v1/2026.clpsych-1.19](https://doi.org/10.18653/v1/2026.clpsych-1.19) · [OpenAlex](https://openalex.org/W7166851308) **Citations:** 0 citations (OpenAlex, 2026-10-14) ## Summary AI models that flag suicide risk from social media often report strong results. This paper asks what those models are actually trained to recognize. The authors pooled two sources. The first is an umbrella review by Abdelmoteleb et al. (2025) covering 22 systematic reviews; eight of these were relevant and yielded 69 primary studies. The second is the authors' own ongoing review, preregistered on OSF, which adds 126 studies from 2022 onward. Across the resulting 195 studies, they examined platforms, data types, languages, dataset reuse and labeling practices. The field is growing fast, but it draws on a narrow base: Reddit, English, text only, and the same few datasets used repeatedly. Most importantly, very few studies confirm suicide risk at the level of the individual. Labels usually come from proxies such as which subreddit a post appeared in, or from expert judgments of what a post says. As a result, models mostly learn to classify suicidal-sounding language rather than to identify people at risk. This means high accuracy scores should be read cautiously. Future progress depends on measures that reflect suicide risk as people actually experience it. ## Key points - The corpus contains 195 studies, about 43% of them (n = 84) from 2024–2026. A supplementary dataset documents each study's characteristics. - Reddit dominates as a data source: - As the sole platform, Reddit accounts for 94 studies (48%), followed by Twitter/X with 46 (24%) and Sina Weibo with 19 (10%). - Counting multi-platform studies, Reddit appears in 113 and Twitter in 64. - Facebook and Instagram are largely missing. - 98% of studies (n = 191) rely on text alone, and none systematically use audio, video or behavioral metadata. English-only work makes up 78% (n = 153), and Chinese is the only other notable language (n = 20). - One public dataset of roughly 232,000 Reddit posts is reused across many studies. It is labeled purely by subreddit membership (e.g., r/SuicideWatch versus general communities). - Only 14 studies (7.2%) validated risk at the individual level, for example with self-report instruments or the Columbia Suicide Severity Rating Scale. These studies consistently report lower model performance. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* ## Institutions - [University of Cambridge](https://md.research.elamur.ai/free/en/universities/university-of-cambridge/) — University, Cambridge, United Kingdom · 256 works · 123,373 citations - [University College London](https://md.research.elamur.ai/free/en/universities/university-college-london/) — University, London, United Kingdom · 107 works · 22,923 citations - [Technion – Israel Institute of Technology](https://md.research.elamur.ai/free/en/universities/technion-israel-institute-of-technology/) — University, Haifa, Israel · 54 works · 7,237 citations - [Ariel University](https://md.research.elamur.ai/free/en/universities/ariel-university/) — University, Ariel, Israel · 10 works · 656 citations - [Reichman University](https://md.research.elamur.ai/free/en/universities/reichman-university/) — University, Herzliya, Israel · 8 works · 16 citations Also on the byline: [Rambam Health Care Campus](https://ror.org/01fm87m50). 📚 Shelves (5): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [💭 Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/) · 1,649 works · [💭 Social Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/social-psychology/) · 330 works · [💭 Clinical Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/clinical-psychology/) · 21 works ## Cite Cite as: Yaakov Ophir, Ofri Hefetz, Refael Tikochinski, Kfir Bar, Shir Lissak, Shulamit Grinapol, Haya Wachtel, Eyal Fruchter and Roi Reichart. 2026. Ground Truths in Suicide Research: The Current State of AI-Based Suicide Detection in Social Media. Proceedings of the 11th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2026). The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{ophir2026ground, title = {{Ground Truths in Suicide Research: The Current State of AI-Based Suicide Detection in Social Media}}, author = {Yaakov Ophir, Ofri Hefetz, Refael Tikochinski, Kfir Bar, Shir Lissak, Shulamit Grinapol, Haya Wachtel, Eyal Fruchter and Roi Reichart}, booktitle = {Proceedings of the 11th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2026)}, year = {2026}, doi = {10.18653/v1/2026.clpsych-1.19}, url = {https://doi.org/10.18653/v1/2026.clpsych-1.19} } ``` - [← previous GROKE: Vision-Free Navigation Instruction Evaluation via Graph Reasoning on OpenStreetMap](https://md.research.elamur.ai/free/en/papers/shami-2026-groke-vision-free-navigation-instruction/) - [next → Grounded Concreteness: Human-Like Concreteness Sensitivity in Vision–Language Models](https://md.research.elamur.ai/free/en/papers/roy-2026-grounded-concreteness-human-like-concreteness/) - [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/35/) ## References 1 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. S. Abdelmoteleb, M. Ghallab, and W. W. IsHak. 2025. Evaluating the ability of artificial intelligence to predict suicide: A systematic review of reviews. Journal of Affective Disorders, 382:525–539. \#\# Limitations [find](https://scholar.google.com/scholar?q=S%2E+Abdelmoteleb%2C+M%2E+Ghallab%2C+and+W%2E+W%2E+IsHak%2E+2025%2E+Evaluating+the+ability+of+artificial+intelligence+to+predict+suicide%3A+A+systematic+review+of+reviews%2E+Journal+of+Affective+Disorders%2C+382%3A525%E2%80%93539%2E+%5C) *Updated 1 November 2026*