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

Creative Commons License

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


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.