Towards employing social media for studying mental health - Yazdavar, Amir Hossein (Kansas State University, 2020)
With the ubiquity of social media platforms, millions of people are now sharing their online persona by expressing their thoughts, moods, emotions, and even their daily struggles with mental health on social media. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from social media obtained unobtrusively. First, we developed a semi-supervised statistical model to evaluate how the duration of these symptoms and their expression on Twitter align with the medical findings reported via the PHQ-9. Based on the analysis of tweets crawled from users, we demonstrate the potential of detecting clinical depression symptoms which emulate the PHQ-9 questionnaire clinicians use today. Over the course of this dissertation, we examine and exploit multi-modal big (social) data to discern depressive behaviors using a wide variety of features including individual-level demographics. By developing a multi-modal framework and employing statistical techniques to fuse heterogeneous sets of features obtained through the processing of visual, textual, and user interaction data, we significantly enhance the current state-of-the-art approaches for identifying depressed individuals on social media as well as facilitate demographic inferences from social media. Besides providing insights into the relationship between demographics and mental health, our research assists in the design of a new breed of demographic-aware health interventions. Altogether, these research topics, resulted in a framework, that when executed, will assist in identifying community-level risk and protective factors associated with the diagnosis and treatment of depression that could be an efficient means of studying patterns of access and utilization of mental health services to inform interventions.
Keywords: Natural language processing; Machine learning; Data science; Statistical inference; Deep learning