Adapting Deep Learning Methods for Mental Health Prediction on Social Media
This work addresses mental health prediction for social media users, but it is incremental as it builds on existing deep learning methods with specific improvements.
The paper tackled the problem of detecting mental health disorders from social media text using deep learning, and found that a hierarchical attention network outperformed previous benchmarks for four out of nine disorders in binary classification tasks.
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 learning-based 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.