Examining and understanding psychosocial well-being in the digital age: A data centric perspective

Psychological disorders are escalating at an unprecedented rate worldwide, underscoring the need for interpretable and scalable approaches for understanding and improving psychological well-being. Concurrently, digital technologies, including cell phones, the Internet, and social media, have become ingrained in everyday life, offering novel insight into individuals and their psychological health. Drawing on social capital theories, this dissertation investigates how digitally mediated social behavior provides a data-driven approach for explicating the relationship between social connections and mental well-being. Eight weeks of data from 216 participants were analyzed using structural equation modeling to uncover the temporal and directional dynamics between social capital and mental health. Specifically, social capital was studied using proxies for its accessed (e.g., incoming or outgoing phone calls) and its latent (e.g., missed phone calls) forms, while mental health was investigated using a validated depression scale. Both the latent growth curve models and cross-lagged panel models showed that depressive symptoms were associated with growth in accessed social capital and decline in latent social capital, suggesting that health could prompt shifts in how social resources are accessed within networks. Meanwhile, the multi-group analysis showed that these associations varied based on extraversion. This dissertation finds that social capital may be a dynamic, context-based resource that exists in varying states and is accessed during times of need. These findings advance the understanding of psychosocial well-being by demonstrating how health influences individuals to engage with connections using data-driven methods and digital traces in the current digital age.