Towards Knowledge-based Mining of Mental Disorder Patterns from Textual Data

TL;DR

This paper proposes a knowledge-based approach leveraging domain expertise and a specialized knowledge base to improve the identification of mental health disorder patterns from social media text, reducing reliance on hand-labelled data.

Contribution

It introduces a novel, domain-specific knowledge base and a weak supervision method to enhance mental disorder detection from textual data, minimizing manual labeling.

Findings

Knowledge-based approach improves detection accuracy

Reduces need for extensive hand-labeled datasets

Effective in social media analysis for depression symptoms

Abstract

Mental health disorders may cause severe consequences on all the countries' economies and health. For example, the impacts of the COVID-19 pandemic, such as isolation and travel ban, can make us feel depressed. Identifying early signs of mental health disorders is vital. For example, depression may increase an individual's risk of suicide. The state-of-the-art research in identifying mental disorder patterns from textual data, uses hand-labelled training sets, especially when a domain expert's knowledge is required to analyse various symptoms. This task could be time-consuming and expensive. To address this challenge, in this paper, we study and analyse the various clinical and non-clinical approaches to identifying mental health disorders. We leverage the domain knowledge and expertise in cognitive science to build a domain-specific Knowledge Base (KB) for the mental health disorder concepts and patterns. We present a weaker form of supervision by facilitating the generating of training data from a domain-specific Knowledge Base (KB). We adopt a typical scenario for analysing social media to identify major depressive disorder symptoms from the textual content generated by social users. We use this scenario to evaluate how our knowledge-based approach significantly improves the quality of results.