Explainable Depression Symptom Detection in Social Media

• URL: http://arxiv.org/abs/2310.13664v3
• Date: Tue, 20 Aug 2024 08:10:27 GMT
• Title: Explainable Depression Symptom Detection in Social Media
• Authors: Eliseo Bao, Anxo Pérez, Javier Parapar,
• Abstract summary: We propose using transformer-based architectures to detect and explain the appearance of depressive symptom markers in the users' writings. Our natural language explanations enable clinicians to interpret the models' decisions based on validated symptoms.
• Score: 2.677715367737641
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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