Self-Adapted Utterance Selection for Suicidal Ideation Detection in Lifeline Conversations

Zhong-Ling Wang, Po-Hsien Huang, Wen-Yau Hsu, Hen-Hsen Huang


Abstract
This paper investigates a crucial aspect of mental health by exploring the detection of suicidal ideation in spoken phone conversations between callers and counselors at a suicide prevention hotline. These conversations can be lengthy, noisy, and cover a broad range of topics, making it challenging for NLP models to accurately identify the caller’s suicidal ideation. To address these difficulties, we introduce a novel, self-adaptive approach that identifies the most critical utterances that the NLP model can more easily distinguish. The experiments use real-world Lifeline transcriptions, expertly labeled, and show that our approach outperforms the baseline models in overall performance with an F-score of 66.01%. In detecting the most dangerous cases, our approach achieves a significantly higher F-score of 65.94% compared to the baseline models, an improvement of 8.9%. The selected utterances can also provide valuable insights for suicide prevention research. Furthermore, our approach demonstrates its versatility by showing its effectiveness in sentiment analysis, making it a valuable tool for NLP applications beyond the healthcare domain.
Anthology ID:
2023.eacl-main.105
Volume:
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
Month:
May
Year:
2023
Address:
Dubrovnik, Croatia
Editors:
Andreas Vlachos, Isabelle Augenstein
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1436–1446
Language:
URL:
https://aclanthology.org/2023.eacl-main.105
DOI:
10.18653/v1/2023.eacl-main.105
Bibkey:
Cite (ACL):
Zhong-Ling Wang, Po-Hsien Huang, Wen-Yau Hsu, and Hen-Hsen Huang. 2023. Self-Adapted Utterance Selection for Suicidal Ideation Detection in Lifeline Conversations. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 1436–1446, Dubrovnik, Croatia. Association for Computational Linguistics.
Cite (Informal):
Self-Adapted Utterance Selection for Suicidal Ideation Detection in Lifeline Conversations (Wang et al., EACL 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.eacl-main.105.pdf
Video:
 https://aclanthology.org/2023.eacl-main.105.mp4