Symposia
Technology
Caitlin A. Stamatis, Ph.D.
Clinical Intern
Northwestern University Feinberg School of Medicine
Jersey City, New Jersey
Jonah Meyerhoff, Ph.D.
Research Assistant Professor
Northwestern University
Chicago, Illinois
Tingting Liu, Ph.D.
Postdoctoral Fellow
Technology & Translational Research Unit, National Institute on Drug Abuse (NIDA IRP), National Institutes of Health (NIH)
Baltimore, Maryland
Garrick Sherman, Ph.D.
Senior Data Scientist
Department of Computer Science, University of Pennsylvania
Philadelphia, Pennsylvania
Harry Wang, B.S.
M.S. Candidate
Department of Computer Science, University of Pennsylvania
Philadelphia, Pennsylvania
Tony Liu, M.S.
Ph.D. Candidate
Department of Computer Science, University of Pennsylvania
Philadelphia, Pennsylvania
Brenda Curtis, Ph.D., MsPH
Chief
Technology & Translational Research Unit, National Institute on Drug Abuse (NIDA IRP), National Institutes of Health (NIH)
Baltimore, Maryland
Lyle Ungar, Ph.D.
Professor
Department of Computer Science, University of Pennsylvania
Philadelphia, Pennsylvania
David Mohr, PhD
Professor
Northwestern University
Chicago, Illinois
Objective: Language patterns may elucidate mechanisms of mental health conditions. To inform underlying theory and risk models, we evaluated prospective associations between in-vivo language and differential symptoms of depression, generalized anxiety, and social anxiety.
Methods: Over 16 weeks, we passively collected the sentiment of outgoing text messages from 340 adults. Using Linguistic Inquiry and Word Count and NRC Emotion Lexicon dictionaries, we evaluated the degree to which language features predict symptoms of depression, generalized anxiety, or social anxiety the following week using hierarchical linear models. To isolate the specificity of language effects, we also controlled for the effects of the two other symptom types.
Results: We found significant relationships of language features, including personal pronouns, negative emotion, cognitive and biological processes, and informal language, with common mental health conditions, including depression, generalized anxiety, and social anxiety (ps< .05). There was substantial overlap between language features and the three mental health outcomes. However, after controlling for other symptoms in the models, depressive symptoms were uniquely negatively associated with language about anticipation, trust, social processes, and affiliation (βs: [-.10, -.09], ps< .05), whereas generalized anxiety symptoms were positively linked with these same language features (βs: [.12, .13], ps< .001). Social anxiety symptoms were uniquely associated with anger, sexual language, and swearing (βs: [.12, .13], ps< .05).
Conclusion: Language that confers both common (e.g., personal pronouns and negative emotion) and specific risk for affective disorders are perceptible in prior week text messages, holding promise for understanding cognitive-behavioral mechanisms and tailoring digital interventions.