Adapting Deep Learning Methods for Mental Health Prediction on Social Media

Abstract

Mental health poses a significant challenge for an individual's well-being.Text analysis of rich resources, like social media, can contribute to deeper understanding of illnesses and provide means for their early detection.We tackle a challenge of detecting social media users' mental status through deep learningbased models, moving away from traditional approaches to the task.In a binary classification task on predicting if a user suffers from one of nine different disorders, a hierarchical attention network outperforms previously set benchmarks for four of the disorders.Furthermore, we explore the limitations of our model and analyze phrases relevant for classification by inspecting the model's word-level attention weights.

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