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

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations

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.

Original languageEnglish
Pages (from-to)4241-4250
Number of pages10
JournalProceedings - International Conference on Computational Linguistics, COLING
Volume29
Issue number1
StatePublished - 1 Jan 2022
Event29th International Conference on Computational Linguistics, COLING 2022 - Gyeongju, Korea, Republic of
Duration: 12 Oct 202217 Oct 2022

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Theoretical Computer Science

Fingerprint

Dive into the research topics of 'Detecting Suicide Risk in Online Counseling Services: A Study in a Low-Resource Language'. Together they form a unique fingerprint.

Cite this