# Inter and Intra Document Attention for Depression Risk Assessment

## TL;DR

This paper presents an attention-based RNN model for early depression risk assessment using social media posts, effectively prioritizing important writings to improve classification accuracy.

## Contribution

It introduces a novel attention mechanism within RNNs to better aggregate user posts for depression risk prediction, advancing prior methods.

## Key findings

- The attention-based model outperforms baseline RNNs.
- Parallel reading of posts improves efficiency.
- Prioritizing important posts enhances prediction accuracy.

## Abstract

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.