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Feature Attention Network: Interpretable Depression Detection from Social Media - 2018

Feature Attention Network: Interpretable Depression Detection From Social Media

Research Paper on Feature Attention Network: Interpretable Depression Detection From Social Media

Research Area:  Machine Learning


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.

Feature Attention Network
Depression Detection
Social Media
Machine Learning
Deep Learning

Author(s) Name:   Hoyun Song, Jinseon You, Jin-Woo Chung, Jong C. Park

Journal name:  

Conferrence name:  Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation

Publisher name:  Association for Computational Linguistics


Volume Information: