Depression is the most prevalent and serious mental illness, which induces grave financial and societal ramifications. Depression detection is key for early intervention to mitigate those consequences. Such a high-stake decision inherently necessitates interpretability, which most existing methods fall short of. To connect human expertise in this decision-making, safeguard trust from end users, and ensure algorithm transparency, we develop an interpretable deep learning model: Multi-Scale Temporal Prototype Network (MSTPNet). MSTPNet is built upon the emergent prototype learning methods. In line with the medical practice of depression diagnosis, MSTPNet differs from existing prototype learning models in its capability of capturing the depressive symptoms and their temporal distribution such as frequency and persistence of appearance. Extensive empirical analyses using real-world social media data show that MSTPNet outperforms state-of-the-art benchmarks in depression detection, with an F1-score of 0.851. Moreover, MSTPNet interprets its prediction by identifying what depression symptoms the user presents and how long these related symptoms last. We further conduct a user study to demonstrate its superiority over the benchmarks in interpretability. Methodologically, this study contributes to extant literature with a novel interpretable deep learning model for depression detection in social media. Our proposed method can be implemented in social media platforms to detect depression and its symptoms. Platforms can subsequently provide personalized online resources such as educational and supporting videos and articles, or sources for treatments and social support for depressed patients.

翻译：抑郁症是最普遍且严重的精神疾病，会造成重大的经济和社会影响。抑郁症检测是早期干预以减轻这些后果的关键。这类高风险决策本质上需要可解释性，而现有方法大多未能满足这一需求。为了将人类专业知识融入决策过程、保障终端用户的信任并确保算法透明度，我们开发了一种可解释的深度学习模型：多尺度时间原型网络（MSTPNet）。MSTPNet基于新兴的原型学习方法构建。与抑郁症诊断的医学实践一致，MSTPNet区别于现有原型学习模型之处在于其能够捕捉抑郁症状及其时间分布（如症状出现的频率和持续性）。利用真实社交媒体数据进行的大量实证分析表明，MSTPNet在抑郁症检测中优于现有最先进基准模型，F1分数达到0.851。此外，MSTPNet通过识别用户呈现的抑郁症状及这些相关症状的持续时间来解释其预测结果。我们进一步开展了用户研究，证明其在可解释性方面优于基准模型。从方法论角度看，本研究为现有文献贡献了一种新颖的可解释深度学习模型，用于社交媒体抑郁症检测。我们提出的方法可部署在社交媒体平台上以检测抑郁症及其症状，平台随后能提供个性化在线资源，如教育性和支持性视频及文章，或为抑郁症患者提供治疗和社会支持的渠道。