Mental Disorder Classification via Temporal Representation of Text

TL;DR

This paper introduces a novel temporal representation framework for classifying mental disorders from social media text, outperforming state-of-the-art methods by capturing inter-post dependencies and temporal dynamics.

Contribution

The paper proposes a new sequence compression approach that preserves temporal information, improving mental disorder prediction accuracy across multiple conditions.

Findings

Outperforms SOTA by 5% in F1 score for depression, self-harm, and anorexia.

Highlights the importance of temporal properties in social media text for mental health classification.

Demonstrates effective cross-domain application of the proposed framework.

Abstract

Mental disorders pose a global challenge, aggravated by the shortage of qualified mental health professionals. Mental disorder prediction from social media posts by current LLMs is challenging due to the complexities of sequential text data and the limited context length of language models. Current language model-based approaches split a single data instance into multiple chunks to compensate for limited context size. The predictive model is then applied to each chunk individually, and the most voted output is selected as the final prediction. This results in the loss of inter-post dependencies and important time variant information, leading to poor performance. We propose a novel framework which first compresses the large sequence of chronologically ordered social media posts into a series of numbers. We then use this time variant representation for mental disorder classification. We demonstrate the generalization capabilities of our framework by outperforming the current SOTA in three different mental conditions: depression, self-harm, and anorexia, with an absolute improvement of 5% in the F1 score. We investigate the situation where current data instances fall within the context length of language models and present empirical results highlighting the importance of temporal properties of textual data. Furthermore, we utilize the proposed framework for a cross-domain study, exploring commonalities across disorders and the possibility of inter-domain data usage.