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


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Bayram U., Benhiba L.

North American Chapter of the Association for Computational Linguistics (NAACL) Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology , Washington, United States Of America, 10 - 15 July 2022, pp.219-225

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.18653/v1/2022.clpsych-1.20
  • City: Washington
  • Country: United States Of America
  • Page Numbers: pp.219-225
  • Çanakkale Onsekiz Mart University Affiliated: Yes

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 BiLSTM 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.