🤖 AI Summary
Existing social media depression detection methods lack clinical interpretability and fail to model symptom duration and dynamic evolution. To address this, we propose the Multi-Scale Temporal Prototype Network (MSTPNet), the first model to explicitly align its decision-making process with DSM-5 diagnostic criteria, enabling symptom-level interpretable detection. MSTPNet employs prototype learning to identify clinically relevant depressive manifestations (e.g., the newly identified indicator “desiring a different life”) and jointly leverages symptom-specific attention and duration inference mechanisms to quantify their temporal evolution. Evaluated on public benchmarks, MSTPNet achieves an F1-score of 0.851, significantly outperforming state-of-the-art methods. A user study with clinical experts confirms high acceptance of its interpretability. The system has been deployed at platform scale for real-world application.
📝 Abstract
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.
