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

Shweta Yadav, Jainish Chauhan, Joy Prakash Sain, Krishnaprasad Thirunarayan, Amit Sheth, Jeremiah Schumm


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, health-care 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 the 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 lay-ers 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.
Anthology ID:
2020.coling-main.61
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
696–709
Language:
URL:
https://aclanthology.org/2020.coling-main.61
DOI:
10.18653/v1/2020.coling-main.61
Bibkey:
Cite (ACL):
Shweta Yadav, Jainish Chauhan, Joy Prakash Sain, Krishnaprasad Thirunarayan, Amit Sheth, and Jeremiah Schumm. 2020. Identifying Depressive Symptoms from Tweets: Figurative Language Enabled Multitask Learning Framework. In Proceedings of the 28th International Conference on Computational Linguistics, pages 696–709, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Identifying Depressive Symptoms from Tweets: Figurative Language Enabled Multitask Learning Framework (Yadav et al., COLING 2020)
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PDF:
https://aclanthology.org/2020.coling-main.61.pdf