Abstract
Discovering individuals’ suicidality on social media has become increasingly important. Many researchers have studied to detect suicidality by using a suicide dictionary. However, while prior work focused on matching a word in a post with a suicide dictionary without considering contexts, little attention has been paid to how the word can be associated with the suicide-related context. To address this problem, we propose a suicidality detection model based on a graph neural network to grasp the dynamic semantic information of the suicide vocabulary by learning the relations between a given post and words. The extensive evaluation demonstrates that the proposed model achieves higher performance than the state-of-the-art methods. We believe the proposed model has great utility in identifying the suicidality of individuals and hence preventing individuals from potential suicide risks at an early stage.- Anthology ID:
- 2022.clpsych-1.10
- Volume:
- Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, USA
- Editors:
- Ayah Zirikly, Dana Atzil-Slonim, Maria Liakata, Steven Bedrick, Bart Desmet, Molly Ireland, Andrew Lee, Sean MacAvaney, Matthew Purver, Rebecca Resnik, Andrew Yates
- Venue:
- CLPsych
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 116–125
- Language:
- URL:
- https://aclanthology.org/2022.clpsych-1.10
- DOI:
- 10.18653/v1/2022.clpsych-1.10
- Cite (ACL):
- Daeun Lee, Migyeong Kang, Minji Kim, and Jinyoung Han. 2022. Detecting Suicidality with a Contextual Graph Neural Network. In Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology, pages 116–125, Seattle, USA. Association for Computational Linguistics.
- Cite (Informal):
- Detecting Suicidality with a Contextual Graph Neural Network (Lee et al., CLPsych 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2022.clpsych-1.10.pdf
- Data