Inter and Intra Document Attention for Depression Risk Assessment
This work addresses mental health monitoring for social media users, but it is incremental as it builds on existing methods with minor enhancements.
The paper tackled early depression risk assessment in social media users by implementing RNN-based systems with attention mechanisms, achieving improved classification on the eRisk 2018 dataset.
We take interest in the early assessment of risk for depression in social media users. We focus on the eRisk 2018 dataset, which represents users as a sequence of their written online contributions. We implement four RNN-based systems to classify the users. We explore several aggregations methods to combine predictions on individual posts. Our best model reads through all writings of a user in parallel but uses an attention mechanism to prioritize the most important ones at each timestep.