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SocialText: A Framework for Understanding the Relationship Between Digital Communication Patterns and Mental Health

As social media platforms have grown to form the foundation of modern digital communication, digital text message datasets that document interpersonal exchanges on these platforms have proliferated. These exchanges comprise a rich corpus of social context data, which can provide insight into how mental health challenges manifest in social contexts. 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, sentiment analysis, time series analysis, and discourse analysis. However, there is a marked divide in current literature between qualitative and quantitative feature extraction methods. To effectively identify and analyze key underlying social contexts and related mental health factors from digital text communication data, researchers must extract a comprehensive corpus of features from raw textual data streams. In this paper, we present a generalized framework for extracting features from digital text communication datasets that leverages methodological approaches from diverse fields. This framework will serve to bridge the gap between quantitative and qualitative research approaches to analyzing digital text communications with respect to mental health.