  Depression is a common mental health issue that requires prompt diagnosis and
treatment. Despite the promise of social media data for depression detection,
the opacity of employed deep learning models hinders interpretability and
raises bias concerns. We address this challenge by introducing ProtoDep, a
novel, explainable framework for Twitter-based depression detection. ProtoDep
leverages prototype learning and the generative power of Large Language Models
to provide transparent explanations at three levels: (i) symptom-level
explanations for each tweet and user, (ii) case-based explanations comparing
the user to similar individuals, and (iii) transparent decision-making through
classification weights. Evaluated on five benchmark datasets, ProtoDep achieves
near state-of-the-art performance while learning meaningful prototypes. This
multi-faceted approach offers significant potential to enhance the reliability
and transparency of depression detection on social media, ultimately aiding
mental health professionals in delivering more informed care.
