Although depression is one of the most common mental disorders, the depressed individuals may not be aware of their symptoms at all so that they sometimes miss the appropriate time for treatment. In order to prevent this problem, many researchers looked into social media to figure out depressed individuals by analyzing the differences in language use. While they have recently achieved reasonable performance in detecting depression, especially using deep learning methods, such methods still do not provide a clear way to explain why certain individuals have been detected as depressed. To address this issue, we propose Feature Attention Network (FAN), inspired by the process of diagnosing depression by an expert who has background knowledge about depression. We evaluate the performance of our model on a large scale general forum (Reddit Self-reported Depression Diagnosis) dataset. Experimental results demonstrate that FAN shows good performance with high interpretability despite a smaller number of posts in training data. We investigate different aspects of posts by depressed users through four feature networks built upon psychological studies, which will help researchers to investigate social media posts to find useful evidence for depressive symptoms.
Recently, REF built a feature attention network for depression detection on Reddit, showing high interpretability, but low improvement in accuracy.
Feature Attention Network: Interpretable Depression Detection from Social Media
PACLIC
Psychology Computer Science