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 analyze broad range of mental health conditions in Twitter texts by identifying self-reported statements of diagnosis.
From ADHD to SAD: Analyzing the Language of Mental Health on Twitter through Self-Reported Diagnoses
CLPsych@HLT-NAACL
Computer Science