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

Understand

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.