They Look Like Each Other: Case-based Reasoning for Explainable Depression Detection on Twitter using Large Language Models

TL;DR

ProtoDep is an explainable depression detection framework for Twitter that combines prototype learning and large language models to provide transparent, multi-level explanations, improving interpretability and trustworthiness.

Contribution

The paper introduces ProtoDep, a novel framework that enhances interpretability in depression detection by integrating prototype learning with large language models for transparent explanations.

Findings

Achieves near state-of-the-art performance on benchmark datasets

Provides symptom-level, case-based, and decision-making explanations

Learns meaningful prototypes for depression detection

Abstract

Depression is a common mental health issue that requires prompt diagnosis and treatment. Despite the promise of social media data for depression detection, the opacity of employed deep learning models hinders interpretability and raises bias concerns. We address this challenge by introducing ProtoDep, a novel, explainable framework for Twitter-based depression detection. ProtoDep leverages prototype learning and the generative power of Large Language Models to provide transparent explanations at three levels: (i) symptom-level explanations for each tweet and user, (ii) case-based explanations comparing the user to similar individuals, and (iii) transparent decision-making through classification weights. Evaluated on five benchmark datasets, ProtoDep achieves near state-of-the-art performance while learning meaningful prototypes. This multi-faceted approach offers significant potential to enhance the reliability and transparency of depression detection on social media, ultimately aiding mental health professionals in delivering more informed care.