A big data analytics framework for detecting user-level depression from social networks. (October 2020) - Yang, Xingwei
Highlights: A big data framework that analyzes tremendous amount of social network data. Provides a reliable knowledge to clinicians and hospitals for depression analysis. User intention and friends influence play important roles in depression detection. Experiments on a massive dataset demonstrate the effectiveness of the system. Abstract: Depression is one of the most common mental health problems worldwide. The diagnosis of depression is usually done by clinicians based on mental status questionnaires and patient's self-reporting. Not only do these methods highly depend on the current mood of the patient, but also people who experience mental illness are often reluctantly seeking help. Social networks have become a popular platform for people to express their feelings and thoughts with friends and family. With the substantial amount of data in social networks, there is an opportunity to try designing novel frameworks to identify those at risk of depression. Moreover, such frameworks can provide clinicians and hospitals with deeper insights about depressive behavioral patterns, thereby improving diagnostic process. In this paper, we propose a big data analytics framework to detect depression for users of social networks. In addition to syntactic and syntax features, it focuses on pragmatic features toward modeling the intention of users. User intention represents the true motivation behind social network behaviors. Moreover, since the behaviors of user's friends in the network are believed to have an influence on the user, the framework also models the influence of friends on the user's mental states. We evaluate the performance of the proposed framework on a massive real dataset obtained from Facebook and show that the framework outperforms existing methods for diagnosing user-level depression in social networks.