Automatic Depression Detection in Social Networks Using Multiple User Characterizations

Depression is rapidly becoming one of the most common illnesses worldwide, currently affecting a significant number of people. These people may show different signs of depression depending on a number of characteristics (e.g., age, sex, personality, mood, etc.). Experts often use these signs to diagnose and monitor depression. However, due to the difficulty of obtaining this information through traditional methods, the use of social networks to characterize users has proven to be a valuable resource. In this paper, we study the potential of various user characterizations for the task of automatic depression detection. We consider two social networks and a variety of models for fusing the characterizations. In particular, we propose the use of a range of networks that learn to weight the contribution of each characterization from the data. We show that, using this model, the depression detection performance outperforms the state-of-the-art results on the two data sets considered. In addition, we present interesting findings on the correlation of the characterizations considered in the information fusion network.