  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
