Predicting Depression with Text, Image, and Profile Data from Social Media.

In this study, we focused on the task of identifying depressed users based on their digital media on a social network. We processed over 60,000 images, 95,000 posts, and 9,000 subscription items related to 619 user profiles on the VKontakte social media network. Beck Depression Inventory screenings were used to assess the presence of depression among these users and divide them into depression and control groups. We retrieved 6 different text based feature sets, images, and general profile data. The experimental evaluation was designed around using all available data from user profiles and creating a prediction pipeline that can process data samples regardless of the availability of text or image data in the user profile. The best result achieved a 69% F1-score with a stacking classifier approach.