World Wide Web

Linguistics

Philosophy

Task analysis

Support vector machine

Binary classification

Psychiatry

Economics

Set (abstract data type)

Computer science

Cognitive science

Natural language processing

Psychology

Task (project management)

Management

Social media

Data science

Artificial intelligence

Mental health

Programming language

Deep learning

Mental model

Word (group theory)

Machine learning

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.

Adapting Deep Learning Methods for Mental Health Prediction on Social Media