Abstract
Social media data have been used in research for many years to understand users’ mental health. In this paper, using user-generated content we aim to achieve two goals: the first is detecting moments of mood change over time using timelines of users from Reddit. The second is predicting the degree of suicide risk as a user-level classification task. We used different approaches to address longitudinal modelling as well as the problem of the severely imbalanced dataset. Using BERT with undersampling techniques performed the best among other LSTM and basic random forests models for the first task. For the second task, extracting some features related to suicide from posts’ text contributed to the overall performance improvement. Specifically, a number of suicide-related words in a post as a feature improved the accuracy by 17%.- Anthology ID:
- 2022.clpsych-1.23
- 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:
- 239–244
- Language:
- URL:
- https://aclanthology.org/2022.clpsych-1.23
- DOI:
- 10.18653/v1/2022.clpsych-1.23
- Cite (ACL):
- Falwah Alhamed, Julia Ive, and Lucia Specia. 2022. Predicting Moments of Mood Changes Overtime from Imbalanced Social Media Data. In Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology, pages 239–244, Seattle, USA. Association for Computational Linguistics.
- Cite (Informal):
- Predicting Moments of Mood Changes Overtime from Imbalanced Social Media Data (Alhamed et al., CLPsych 2022)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2022.clpsych-1.23.pdf