Dynamic Graph Representation Learning for Depression Screening with Transformer

TL;DR

This paper introduces ContrastEgo, a novel framework that models social media users as dynamic graphs and employs contrastive learning to improve early depression detection, addressing limitations of feature engineering and time-varying factors.

Contribution

The paper proposes a dynamic graph-based depression detection method using contrastive learning, capturing temporal social interactions without extensive feature engineering.

Findings

ContrastEgo outperforms state-of-the-art methods on Twitter data

Effective modeling of time-varying social interactions improves detection accuracy

Contrastive learning enhances user representation differentiation

Abstract

Early detection of mental disorder is crucial as it enables prompt intervention and treatment, which can greatly improve outcomes for individuals suffering from debilitating mental affliction. The recent proliferation of mental health discussions on social media platforms presents research opportunities to investigate mental health and potentially detect instances of mental illness. However, existing depression detection methods are constrained due to two major limitations: (1) the reliance on feature engineering and (2) the lack of consideration for time-varying factors. Specifically, these methods require extensive feature engineering and domain knowledge, which heavily rely on the amount, quality, and type of user-generated content. Moreover, these methods ignore the important impact of time-varying factors on depression detection, such as the dynamics of linguistic patterns and interpersonal interactive behaviors over time on social media (e.g., replies, mentions, and quote-tweets). To tackle these limitations, we propose an early depression detection framework, ContrastEgo treats each user as a dynamic time-evolving attributed graph (ego-network) and leverages supervised contrastive learning to maximize the agreement of users' representations at different scales while minimizing the agreement of users' representations to differentiate between depressed and control groups. ContrastEgo embraces four modules, (1) constructing users' heterogeneous interactive graphs, (2) extracting the representations of users' interaction snapshots using graph neural networks, (3) modeling the sequences of snapshots using attention mechanism, and (4) depression detection using contrastive learning. Extensive experiments on Twitter data demonstrate that ContrastEgo significantly outperforms the state-of-the-art methods in terms of all the effectiveness metrics in various experimental settings.