Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings

TL;DR

This paper introduces an explainable hierarchical attention network for depression detection on Twitter, incorporating metaphorical concept mappings to enhance interpretability and support psycholinguistic insights.

Contribution

It presents a novel neural model that combines hierarchical attention and metaphor analysis for more transparent depression detection on social media.

Findings

The model effectively identifies depressed users on Twitter.

It provides interpretable features linked to metaphorical concepts.

The approach supports psycholinguistic research through feature analysis.

Abstract

Automatic depression detection on Twitter can help individuals privately and conveniently understand their mental health status in the early stages before seeing mental health professionals. Most existing black-box-like deep learning methods for depression detection largely focused on improving classification performance. However, explaining model decisions is imperative in health research because decision-making can often be high-stakes and life-and-death. Reliable automatic diagnosis of mental health problems including depression should be supported by credible explanations justifying models' predictions. In this work, we propose a novel explainable model for depression detection on Twitter. It comprises a novel encoder combining hierarchical attention mechanisms and feed-forward neural networks. To support psycholinguistic studies, our model leverages metaphorical concept mappings as input. Thus, it not only detects depressed individuals, but also identifies features of such users' tweets and associated metaphor concept mappings.