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
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
4241–4250
Language:
URL:
https://aclanthology.org/2022.coling-1.372
DOI:
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.372.pdf
Code
 amirbialer/coling_2022_early-detection-of-suicide-risk-in-online-counseling-services