Identifying Depressive Symptoms from Tweets: Figurative Language Enabled Multitask Learning Framework

TL;DR

This paper presents a BERT-based multi-task learning framework that improves depression symptom detection from tweets by incorporating figurative language analysis, aiding scalable mental health screening.

Contribution

The study introduces a novel co-task aware attention mechanism for multi-task learning that enhances depression detection accuracy by modeling figurative language.

Findings

Figurative language modeling improves detection robustness.

Multi-task learning outperforms single-task baselines.

The framework aligns with clinical depression screening tools.

Abstract

Existing studies on using social media for deriving mental health status of users focus on the depression detection task. However, for case management and referral to psychiatrists, healthcare workers require practical and scalable depressive disorder screening and triage system. This study aims to design and evaluate a decision support system (DSS) to reliably determine the depressive triage level by capturing fine-grained depressive symptoms expressed in user tweets through the emulation of Patient Health Questionnaire-9 (PHQ-9) that is routinely used in clinical practice. The reliable detection of depressive symptoms from tweets is challenging because the 280-character limit on tweets incentivizes the use of creative artifacts in the utterances and figurative usage contributes to effective expression. We propose a novel BERT based robust multi-task learning framework to accurately identify the depressive symptoms using the auxiliary task of figurative usage detection. Specifically, our proposed novel task sharing mechanism, co-task aware attention, enables automatic selection of optimal information across the BERT layers and tasks by soft-sharing of parameters. Our results show that modeling figurative usage can demonstrably improve the model's robustness and reliability for distinguishing the depression symptoms.