  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.
