A Generalized Framework for Understanding the Relationship between Social Media Discourse and Mental Health

Over 35% of the world’s population uses social media. Platforms like Facebook, Twitter, and Instagram have radically impacted the way individuals interact and communicate. These platforms facilitate both public and private communication with strangers and friends alike, providing rich insight into an individual’s personality, health, and wellbeing. To date, researchers have employed a variety of methods for extracting mental health-centric features from digital text communication data, including natural language processing, social network analysis, and extraction of temporal discourse patterns. However, none have explored a hierarchical framework for extracting features from private messages with the goal of unifying approaches across methodological domains. Furthermore, while analyses of large, public corpora abound in existing literature, limited work has been done to explore the relationship between of private textual communications, personality traits, and symptoms of mental illness. We present a framework for constructing rich feature spaces from digital text communications. We then demonstrate the efficacy of our framework by applying it to a sample of private Facebook messages in a college student population (N = 103). Our results reveal key individual differences in temporal and relational behaviors, as well as language usage in relation to validated measures of trait-level anxiety, loneliness, and personality. By leveraging the comprehensive structure outlined by our framework, we not only built more complete models of private social media discourse but also demonstrated the associated affordances with respect to classifying mental health. This work represents a critical step forward in linking features of private social media messages to validated measures of mental health and wellbeing.