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

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dc.contributor.authorBayram, Ulya
dc.contributor.authorBenhiba, Lamia
dc.date.accessioned2025-01-27T18:53:04Z
dc.date.available2025-01-27T18:53:04Z
dc.date.issued2022
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.descriptionAmerican Association of Suicidology; Rebecca Resnik and Associates Psychological Care; Receptiviti; University of Maryland Institute for Advanced Computer Studies (UMIACS)
dc.description8th Workshop on Computational Linguistics and Clinical Psychology, CLPsych 2022 -- 15 July 2022 -- Seattle -- 182104
dc.description.abstractIn 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. © 2022 Association for Computational Linguistics.
dc.description.sponsorshipAmerican Association of Suicidology; Nutrition Obesity Research Center, University of North Carolina, NORC; UK Research and Innovation, UKRI; Bar-Ilan University
dc.identifier.endpage225
dc.identifier.isbn978-195591787-2
dc.identifier.scopus2-s2.0-85137975576
dc.identifier.scopusqualityN/A
dc.identifier.startpage219
dc.identifier.urihttps://hdl.handle.net/20.500.12428/12567
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherAssociation for Computational Linguistics (ACL)
dc.relation.ispartofCLPsych 2022 - 8th Workshop on Computational Linguistics and Clinical Psychology, Proceedings
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250125
dc.subjectEmotion Recognition; Learning systems; Long short-term memory; Social networking (online); Auto encoders; Emotion recognition; Logistics regressions; Machine learning models; Mental health; N-grams; Participating teams; Risk levels; Sentiment scores; Social media datum; Risk assessment
dc.titleEmotionally-Informed Models for Detecting Moments of Change and Suicide Risk Levels in Longitudinal Social Media Data
dc.typeConference Object

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