  Depression is a widespread mental health issue, affecting an estimated 3.8%
of the global population. It is also one of the main contributors to disability
worldwide. Recently it is becoming popular for individuals to use social media
platforms (e.g., Reddit) to express their difficulties and health issues (e.g.,
depression) and seek support from other users in online communities. It opens
great opportunities to automatically identify social media users with
depression by parsing millions of posts for potential interventions. Deep
learning methods have begun to dominate in the field of machine learning and
natural language processing (NLP) because of their ease of use, efficient
processing, and state-of-the-art results on many NLP tasks. In this work, we
propose a hybrid deep learning model which combines a pretrained sentence BERT
(SBERT) and convolutional neural network (CNN) to detect individuals with
depression with their Reddit posts. The sentence BERT is used to learn the
meaningful representation of semantic information in each post. CNN enables the
further transformation of those embeddings and the temporal identification of
behavioral patterns of users. We trained and evaluated the model performance to
identify Reddit users with depression by utilizing the Self-reported Mental
Health Diagnoses (SMHD) data. The hybrid deep learning model achieved an
accuracy of 0.86 and an F1 score of 0.86 and outperformed the state-of-the-art
documented result (F1 score of 0.79) by other machine learning models in the
literature. The results show the feasibility of the hybrid model to identify
individuals with depression. Although the hybrid model is validated to detect
depression with Reddit posts, it can be easily tuned and applied to other text
classification tasks and different clinical applications.
