Detecting Suicide Risk in Online Counseling Services: A Study in a Low-Resource Language
Amir Bialer, Daniel Izmaylov, Avi Segal, Oren Tsur, Yossi Levi-Belz, Kobi Gal
Abstract
With the increased awareness of situations of mental crisis and their societal impact, online services providing emergency support are becoming commonplace in many countries. Computational models, trained on discussions between help-seekers and providers, can support suicide prevention by identifying at-risk individuals. However, the lack of domain-specific models, especially in low-resource languages, poses a significant challenge for the automatic detection of suicide risk. We propose a model that combines pre-trained language models (PLM) with a fixed set of manually crafted (and clinically approved) set of suicidal cues, followed by a two-stage fine-tuning process. Our model achieves 0.91 ROC-AUC and an F2-score of 0.55, significantly outperforming an array of strong baselines even early on in the conversation, which is critical for real-time detection in the field. Moreover, the model performs well across genders and age groups.- Anthology ID:
- 2022.coling-1.372
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 4241–4250
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.372
- DOI:
- Cite (ACL):
- Amir Bialer, Daniel Izmaylov, Avi Segal, Oren Tsur, Yossi Levi-Belz, and Kobi Gal. 2022. Detecting Suicide Risk in Online Counseling Services: A Study in a Low-Resource Language. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4241–4250, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Detecting Suicide Risk in Online Counseling Services: A Study in a Low-Resource Language (Bialer et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2022.coling-1.372.pdf
- Code
- amirbialer/coling_2022_early-detection-of-suicide-risk-in-online-counseling-services