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Twitter is currently a popular online social media platform which allowsusers to share their user-generated content. This publicly-generated user datais also crucial to healthcare technologies because the discovered patternswould hugely benefit them in several ways. One of the applications is inautomatically discovering mental health problems, e.g., depression. Previousstudies to automatically detect a depressed user on online social media havelargely relied upon the user behaviour and their linguistic patterns includinguser's social interactions. The downside is that these models are trained onseveral irrelevant content which might not be crucial towards detecting adepressed user. Besides, these content have a negative impact on the overallefficiency and effectiveness of the model. To overcome the shortcomings in theexisting automatic depression detection methods, we propose a novelcomputational framework for automatic depression detection that initiallyselects relevant content through a hybrid extractive and abstractivesummarization strategy on the sequence of all user tweets leading to a morefine-grained and relevant content. The content then goes to our novel deeplearning framework comprising of a unified learning machinery comprising ofConvolutional Neural Network (CNN) coupled with attention-enhanced GatedRecurrent Units (GRU) models leading to better empirical performance thanexisting strong baselines.

DepressionNet: A Novel Summarization Boosted Deep Framework for Depression Detection on Social Media