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.

本文介绍了解决模型预测不透明性的方法，提出了一种利用多模态特征和分层注意力网络的模型，可以检测社交媒体上的抑郁症患者并解释模型预测，该模型在检测具有挑战性的公共社交媒体数据集上取得了显著优势。