Identifying Depressive Symptoms from Tweets: Figurative Language Enabled Multitask Learning Framework
Autor: | Krishnaprasad Thirunarayan, Amit Sheth, Jainish Chauhan, Joy Prakash Sain, Jeremiah A. Schumm, Shweta Yadav |
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Rok vydání: | 2020 |
Předmět: |
FOS: Computer and information sciences
Decision support system Computer Science - Computation and Language Computer science business.industry Multi-task learning 020206 networking & telecommunications 02 engineering and technology computer.software_genre Triage Mental health Literal and figurative language Task (project management) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Social media Disorder screening Artificial intelligence business Computation and Language (cs.CL) computer Depressive symptoms Natural language processing |
Zdroj: | COLING |
DOI: | 10.18653/v1/2020.coling-main.61 |
Popis: | 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. Accepted for publication in COLING 2020 |
Databáze: | OpenAIRE |
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