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.

翻译：精神疾病的早期检测至关重要，因为它能够促进及时的干预和治疗，从而显著改善患有严重精神障碍患者的预后。社交媒体平台上心理健康讨论的激增，为研究心理健康问题并检测潜在精神疾病提供了新机遇。然而，现有的抑郁症检测方法受限于两大局限性：(1) 依赖特征工程，(2) 缺乏对时变因素的考量。具体而言，这些方法需要大量特征工程和领域知识，其效果高度依赖于用户生成内容的数量、质量和类型。此外，这类方法忽视了时变因素对抑郁症检测的重要影响，例如社交媒体上语言模式及人际交互行为（如回复、提及和引用推文）随时间演变的动态特征。为解决这些局限，我们提出了一种早期抑郁症检测框架ContrastEgo。该框架将每位用户建模为动态时间演化属性图（自我网络），并利用监督对比学习最大化用户在不同尺度上的表征一致性，同时最小化用户间的表征相似度以区分抑郁组与对照组。ContrastEgo包含四个模块：(1) 构建用户异质交互图，(2) 利用图神经网络提取用户交互快照的表征，(3) 采用注意力机制对快照序列进行建模，(4) 通过对比学习实现抑郁症检测。在Twitter数据上的大量实验表明，ContrastEgo在各种实验设置下的所有有效性指标上均显著优于当前最优方法。