  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.
