On the Explainability of Automatic Predictions of Mental Disorders from Social Media Data

Mental disorders are an important public health issue, and computational methods have the potential to aid with detection of risky behaviors online, through extracting information from social media in order to retrieve users at risk of developing mental disorders. At the same time, state-of-the-art machine learning models are based on neural networks, which are notoriously difficult to interpret. Exploring the explainability of neural network models for mental disorder detection can make their decisions more reliable and easier to trust, and can help identify specific patterns in the data which are indicative of mental disorders. We aim to provide interpretations for the manifestations of mental disorder symptoms in language, as well as explain the decisions of deep learning models from multiple perspectives, going beyond classical techniques such as attention analysis, and including activation patterns in hidden layers, and error analysis focused on particular features such as the emotions and topics found in texts, from a technical as well as psycholinguistic perspective, for different social media datasets (sourced from Reddit and Twitter), annotated for four mental disorders: depression, anorexia, PTSD and self-harm tendencies.