Suicidal Risk Detection for Military Personnel

Sungjoon Park, Kiwoong Park, Jaimeen Ahn, Alice Oh


Abstract
We analyze social media for detecting the suicidal risk of military personnel, which is especially crucial for countries with compulsory military service such as the Republic of Korea. From a widely-used Korean social Q&A site, we collect posts containing military-relevant content written by active-duty military personnel. We then annotate the posts with two groups of experts: military experts and mental health experts. Our dataset includes 2,791 posts with 13,955 corresponding expert annotations of suicidal risk levels, and this dataset is available to researchers who consent to research ethics agreement. Using various fine-tuned state-of-the-art language models, we predict the level of suicide risk, reaching .88 F1 score for classifying the risks.
Anthology ID:
2020.emnlp-main.198
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Editors:
Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2523–2531
Language:
URL:
https://aclanthology.org/2020.emnlp-main.198
DOI:
10.18653/v1/2020.emnlp-main.198
Bibkey:
Cite (ACL):
Sungjoon Park, Kiwoong Park, Jaimeen Ahn, and Alice Oh. 2020. Suicidal Risk Detection for Military Personnel. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2523–2531, Online. Association for Computational Linguistics.
Cite (Informal):
Suicidal Risk Detection for Military Personnel (Park et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.198.pdf
Video:
 https://slideslive.com/38939006