Cross-Lingual Suicidal-Oriented Word Embedding toward Suicide Prevention

Daeun Lee, Soyoung Park, Jiwon Kang, Daejin Choi, Jinyoung Han

Abstract
Early intervention for suicide risks with social media data has increasingly received great attention. Using a suicide dictionary created by mental health experts is one of the effective ways to detect suicidal ideation. However, little attention has been paid to validate whether and how the existing dictionaries for other languages (i.e., English and Chinese) can be used for predicting suicidal ideation for a low-resource language (i.e., Korean) where a knowledge-based suicide dictionary has not yet been developed. To this end, we propose a cross-lingual suicidal ideation detection model that can identify whether a given social media post includes suicidal ideation or not. To utilize the existing suicide dictionaries developed for other languages (i.e., English and Chinese) in word embedding, our model translates a post written in the target language (i.e., Korean) into English and Chinese, and then uses the separate suicidal-oriented word embeddings developed for English and Chinese, respectively. By applying an ensemble approach for different languages, the model achieves high accuracy, over 87%. We believe our model is useful in accessing suicidal ideation using social media data for preventing potential suicide risk in an early stage.
Anthology ID:
2020.findings-emnlp.200
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Editors:
Trevor Cohn, Yulan He, Yang Liu
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2208–2217
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.200
DOI:
10.18653/v1/2020.findings-emnlp.200
Bibkey:
Cite (ACL):
Daeun Lee, Soyoung Park, Jiwon Kang, Daejin Choi, and Jinyoung Han. 2020. Cross-Lingual Suicidal-Oriented Word Embedding toward Suicide Prevention. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2208–2217, Online. Association for Computational Linguistics.
Cite (Informal):
Cross-Lingual Suicidal-Oriented Word Embedding toward Suicide Prevention (Lee et al., Findings 2020)
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https://preview.aclanthology.org/dois-2013-emnlp/2020.findings-emnlp.200.pdf
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