PHASE: Learning Emotional Phase-aware Representations for Suicide Ideation Detection on Social Media

Ramit Sawhney, Harshit Joshi, Lucie Flek, Rajiv Ratn Shah


Abstract
Recent psychological studies indicate that individuals exhibiting suicidal ideation increasingly turn to social media rather than mental health practitioners. Contextualizing the build-up of such ideation is critical for the identification of users at risk. In this work, we focus on identifying suicidal intent in tweets by augmenting linguistic models with emotional phases modeled from users’ historical context. We propose PHASE, a time-and phase-aware framework that adaptively learns features from a user’s historical emotional spectrum on Twitter for preliminary screening of suicidal risk. Building on clinical studies, PHASE learns phase-like progressions in users’ historical Plutchik-wheel-based emotions to contextualize suicidal intent. While outperforming state-of-the-art methods, we show the utility of temporal and phase-based emotional contextual cues for suicide ideation detection. We further discuss practical and ethical considerations.
Anthology ID:
2021.eacl-main.205
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Editors:
Paola Merlo, Jorg Tiedemann, Reut Tsarfaty
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2415–2428
Language:
URL:
https://aclanthology.org/2021.eacl-main.205
DOI:
10.18653/v1/2021.eacl-main.205
Bibkey:
Cite (ACL):
Ramit Sawhney, Harshit Joshi, Lucie Flek, and Rajiv Ratn Shah. 2021. PHASE: Learning Emotional Phase-aware Representations for Suicide Ideation Detection on Social Media. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2415–2428, Online. Association for Computational Linguistics.
Cite (Informal):
PHASE: Learning Emotional Phase-aware Representations for Suicide Ideation Detection on Social Media (Sawhney et al., EACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-main.205.pdf
Code
 midas-research/phase-eacl