Classifying Depressed Users With Multiple Instance Learning from Social Network Data

Over 320 million people are suffering from depression worldwide. Depression is one of the common mental health disorders. By its nature, depression can reoccur. People suffering from depression tend to lose interest, have low mood, feel hopeless, or have social isolation. At its worst, depression can lead to suicide. So far, there are a few numbers of studies investigating deep learning techniques to classify social network users with depression. Most of the studies used classical machine learning techniques e.g., regression, support vector machine, or decision trees. This paper aims to develop a deep learning predictive model to classify users with depression. Because depression is a recurrent disease, it is interesting in finding unusual patterns in user-generated content over time. Social network posts over time were extracted for time series data. The predictive model for the classification was obtained from deep learning techniques.