Emotionally-Informed Models for Detecting Moments of Change and Suicide Risk Levels in Longitudinal Social Media Data

Ulya Bayram, Lamia Benhiba


Abstract
In this shared task, we focus on detecting mental health signals in Reddit users’ posts through two main challenges: A) capturing mood changes (anomalies) from the longitudinal set of posts (called timelines), and B) assessing the users’ suicide risk-levels. Our approaches leverage emotion recognition on linguistic content by computing emotion/sentiment scores using pre-trained BERTs on users’ posts and feeding them to machine learning models, including XGBoost, Bi-LSTM, and logistic regression. For Task-A, we detect longitudinal anomalies using a sequence-to-sequence (seq2seq) autoencoder and capture regions of mood deviations. For Task-B, our two models utilize the BERT emotion/sentiment scores. The first computes emotion bandwidths and merges them with n-gram features, and employs logistic regression to detect users’ suicide risk levels. The second model predicts suicide risk on the timeline level using a Bi-LSTM on Task-A results and sentiment scores. Our results outperformed most participating teams and ranked in the top three in Task-A. In Task-B, our methods surpass all others and return the best macro and micro F1 scores.
Anthology ID:
2022.clpsych-1.20
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:
219–225
Language:
URL:
https://aclanthology.org/2022.clpsych-1.20
DOI:
10.18653/v1/2022.clpsych-1.20
Bibkey:
Cite (ACL):
Ulya Bayram and Lamia Benhiba. 2022. Emotionally-Informed Models for Detecting Moments of Change and Suicide Risk Levels in Longitudinal Social Media Data. In Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology, pages 219–225, Seattle, USA. Association for Computational Linguistics.
Cite (Informal):
Emotionally-Informed Models for Detecting Moments of Change and Suicide Risk Levels in Longitudinal Social Media Data (Bayram & Benhiba, CLPsych 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.clpsych-1.20.pdf
Video:
 https://aclanthology.org/2022.clpsych-1.20.mp4