Depression detection in social media posts using affective and social norm features
This work addresses depression detection for mental health applications using social media data, but it is incremental as it builds on existing methods like BERT with added features.
The authors tackled depression detection from social media posts by proposing a deep architecture that combines BERT-based language representations with affective, profanity, and morality features, achieving state-of-the-art results with 2.65% and 6.73% absolute F1 score improvements on two datasets.
We propose a deep architecture for depression detection from social media posts. The proposed architecture builds upon BERT to extract language representations from social media posts and combines these representations using an attentive bidirectional GRU network. We incorporate affective information, by augmenting the text representations with features extracted from a pretrained emotion classifier. Motivated by psychological literature we propose to incorporate profanity and morality features of posts and words in our architecture using a late fusion scheme. Our analysis indicates that morality and profanity can be important features for depression detection. We apply our model for depression detection on Reddit posts on the Pirina dataset, and further consider the setting of detecting depressed users, given multiple posts per user, proposed in the Reddit RSDD dataset. The inclusion of the proposed features yields state-of-the-art results in both settings, namely 2.65% and 6.73% absolute improvement in F1 score respectively. Index Terms: Depression detection, BERT, Feature fusion, Emotion recognition, profanity, morality