Abstract
Recent psychological studies indicate that individuals exhibiting suicidal ideation increasingly turn to social media rather than mental health practitioners. Personally contextualizing the buildup of such ideation is critical for accurate identification of users at risk. In this work, we propose a framework jointly leveraging a user’s emotional history and social information from a user’s neighborhood in a network to contextualize the interpretation of the latest tweet of a user on Twitter. Reflecting upon the scale-free nature of social network relationships, we propose the use of Hyperbolic Graph Convolution Networks, in combination with the Hawkes process to learn the historical emotional spectrum of a user in a time-sensitive manner. Our system significantly outperforms state-of-the-art methods on this task, showing the benefits of both socially and personally contextualized representations.- Anthology ID:
- 2021.naacl-main.176
- Volume:
- Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2176–2190
- Language:
- URL:
- https://aclanthology.org/2021.naacl-main.176
- DOI:
- 10.18653/v1/2021.naacl-main.176
- Cite (ACL):
- Ramit Sawhney, Harshit Joshi, Rajiv Ratn Shah, and Lucie Flek. 2021. Suicide Ideation Detection via Social and Temporal User Representations using Hyperbolic Learning. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2176–2190, Online. Association for Computational Linguistics.
- Cite (Informal):
- Suicide Ideation Detection via Social and Temporal User Representations using Hyperbolic Learning (Sawhney et al., NAACL 2021)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2021.naacl-main.176.pdf