  Model interpretability has become important to engenders appropriate user
trust by providing the insight into the model prediction. However, most of the
existing machine learning methods provide no interpretability for depression
prediction, hence their predictions are obscure to human. In this work, we
propose interpretive Multi-Modal Depression Detection with Hierarchical
Attention Network MDHAN, for detection depressed users on social media and
explain the model prediction. We have considered user posts along with
Twitter-based multi-modal features, specifically, we encode user posts using
two levels of attention mechanisms applied at the tweet-level and word-level,
calculate each tweet and words' importance, and capture semantic sequence
features from the user timelines (posts). Our experiments show that MDHAN
outperforms several popular and robust baseline methods, demonstrating the
effectiveness of combining deep learning with multi-modal features. We also
show that our model helps improve predictive performance when detecting
depression in users who are posting messages publicly on social media. MDHAN
achieves excellent performance and ensures adequate evidence to explain the
prediction.
