DepressionX: Knowledge Infused Residual Attention for Explainable Depression Severity Assessment
This work addresses the need for transparent and accurate mental health assessments from social media data, which is critical for healthcare applications.
The paper tackles the problem of detecting depression severity from social media posts by proposing DepressionX, a knowledge-infused residual attention model that improves explainability and achieves over 7% higher F1 score than state-of-the-art models on balanced and imbalanced datasets.
In today's interconnected society, social media platforms have become an important part of our lives, where individuals virtually express their thoughts, emotions, and moods. These expressions offer valuable insights into their mental health. This paper explores the use of platforms like Facebook, $\mathbb{X}$ (formerly Twitter), and Reddit for mental health assessments. We propose a domain knowledge-infused residual attention model called DepressionX for explainable depression severity detection. Existing deep learning models on this problem have shown considerable performance, but they often lack transparency in their decision-making processes. In healthcare, where decisions are critical, the need for explainability is crucial. In our model, we address the critical gap by focusing on the explainability of depression severity detection while aiming for a high performance accuracy. In addition to being explainable, our model consistently outperforms the state-of-the-art models by over 7% in terms of $\text{F}_1$ score on balanced as well as imbalanced datasets. Our ultimate goal is to establish a foundation for trustworthy and comprehensible analysis of mental disorders via social media.