Many significant challenges exist for the mental health field, but one in particular is a lack of data available to guide research. Language provides a natural lens for studying mental health -much existing work and therapy have strong linguistic components, so the creation of a large, varied, language-centric dataset could provide significant grist for the field of mental health research. We examine a broad range of mental health conditions in Twitter data by identifying self-reported statements of diagnosis. We systematically explore language differences between ten conditions with respect to the general population, and to each other. Our aim is to provide guidance and a roadmap for where deeper exploration is likely to be fruitful.
Coppersmith et al. REF examined a broad range of mental health conditions in Twitter data by identifying self-reported statements of diagnosis, and they took the experimental results as evidence that examining mental health through the lens of language was fertile ground for advances in mental health.
From ADHD to SAD: Analyzing the Language of Mental Health on Twitter through Self-Reported Diagnoses
CLPsych@HLT-NAACL
Computer Science