Suicide Risk Prediction by Tracking Self-Harm Aspects in Tweets: NUS-IDS at the CLPsych 2021 Shared Task
Sujatha Das Gollapalli, Guilherme Augusto Zagatti, See-Kiong Ng
Abstract
We describe our system for identifying users at-risk for suicide based on their tweets developed for the CLPsych 2021 Shared Task. Based on research in mental health studies linking self-harm tendencies with suicide, in our system, we attempt to characterize self-harm aspects expressed in user tweets over a period of time. To this end, we design SHTM, a Self-Harm Topic Model that combines Latent Dirichlet Allocation with a self-harm dictionary for modeling daily tweets of users. Next, differences in moods and topics over time are captured as features to train a deep learning model for suicide prediction.- Anthology ID:
- 2021.clpsych-1.10
- Volume:
- Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- Nazli Goharian, Philip Resnik, Andrew Yates, Molly Ireland, Kate Niederhoffer, Rebecca Resnik
- Venue:
- CLPsych
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 93–98
- Language:
- URL:
- https://aclanthology.org/2021.clpsych-1.10
- DOI:
- 10.18653/v1/2021.clpsych-1.10
- Cite (ACL):
- Sujatha Das Gollapalli, Guilherme Augusto Zagatti, and See-Kiong Ng. 2021. Suicide Risk Prediction by Tracking Self-Harm Aspects in Tweets: NUS-IDS at the CLPsych 2021 Shared Task. In Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access, pages 93–98, Online. Association for Computational Linguistics.
- Cite (Informal):
- Suicide Risk Prediction by Tracking Self-Harm Aspects in Tweets: NUS-IDS at the CLPsych 2021 Shared Task (Gollapalli et al., CLPsych 2021)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2021.clpsych-1.10.pdf