  Twitter is currently a popular online social media platform which allows
users to share their user-generated content. This publicly-generated user data
is also crucial to healthcare technologies because the discovered patterns
would hugely benefit them in several ways. One of the applications is in
automatically discovering mental health problems, e.g., depression. Previous
studies to automatically detect a depressed user on online social media have
largely relied upon the user behaviour and their linguistic patterns including
user's social interactions. The downside is that these models are trained on
several irrelevant content which might not be crucial towards detecting a
depressed user. Besides, these content have a negative impact on the overall
efficiency and effectiveness of the model. To overcome the shortcomings in the
existing automatic depression detection methods, we propose a novel
computational framework for automatic depression detection that initially
selects relevant content through a hybrid extractive and abstractive
summarization strategy on the sequence of all user tweets leading to a more
fine-grained and relevant content. The content then goes to our novel deep
learning framework comprising of a unified learning machinery comprising of
Convolutional Neural Network (CNN) coupled with attention-enhanced Gated
Recurrent Units (GRU) models leading to better empirical performance than
existing strong baselines.
