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. Although a few depression detection studies attempt to explain the decision based on the importance score or attention weights, these explanations misalign with the clinical depression diagnosis criterion that is based on depressive symptoms. To fill this gap, we follow the computational design science paradigm to develop a novel Multi-Scale Temporal Prototype Network (MSTPNet). MSTPNet innovatively detects and interprets depressive symptoms as well as how long they last. Extensive empirical analyses using a large-scale dataset show that MSTPNet outperforms state-of-the-art depression detection methods with an F1-score of 0.851. This result also reveals new symptoms that are unnoted in the survey approach, such as sharing admiration for a different life. We further conduct a user study to demonstrate its superiority over the benchmarks in interpretability. This study contributes to IS literature with a novel interpretable deep learning model for depression detection in social media. In practice, our proposed method can be implemented in social media platforms to provide personalized online resources for detected depressed patients.

翻译：抑郁症是最普遍且严重的精神疾病，会造成重大的经济与社会影响。抑郁症检测对于早期干预以减轻这些后果至关重要。此类高风险决策本质上需要可解释性。尽管少数抑郁症检测研究尝试基于重要性评分或注意力权重来解释决策，但这些解释与基于抑郁症状的临床诊断标准存在偏差。为填补这一空白，我们遵循计算设计科学范式，开发了新型多尺度时间原型网络（Multi-Scale Temporal Prototype Network, MSTPNet）。MSTPNet创新性地检测并解读抑郁症状及其持续时间。基于大规模数据集的广泛实证分析表明，MSTPNet以0.851的F1分数优于现有最先进的抑郁症检测方法。该结果还揭示了问卷调查方法中未被注意的新症状，例如对另一种生活的赞赏分享。我们进一步通过用户研究证明其在可解释性上优于基准方法。本研究为信息系统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。在实际应用中，所提出的方法可部署于社交媒体平台，为检测到的抑郁症患者提供个性化在线资源。