  Users of social platforms often perceive these sites as supportive spaces to
post about their mental health issues. Those conversations contain important
traces about individuals' health risks. Recently, researchers have exploited
this online information to construct mental health detection models, which aim
to identify users at risk on platforms like Twitter, Reddit or Facebook. Most
of these models are centred on achieving good classification results, ignoring
the explainability and interpretability of the decisions. Recent research has
pointed out the importance of using clinical markers, such as the use of
symptoms, to improve trust in the computational models by health professionals.
In this paper, we propose using transformer-based architectures to detect and
explain the appearance of depressive symptom markers in the users' writings. We
present two approaches: i) train a model to classify, and another one to
explain the classifier's decision separately and ii) unify the two tasks
simultaneously using a single model. Additionally, for this latter manner, we
also investigated the performance of recent conversational LLMs when using
in-context learning. Our natural language explanations enable clinicians to
interpret the models' decisions based on validated symptoms, enhancing trust in
the automated process. We evaluate our approach using recent symptom-based
datasets, employing both offline and expert-in-the-loop metrics to assess the
quality of the explanations generated by our models. The experimental results
show that it is possible to achieve good classification results while
generating interpretable symptom-based explanations.
