Using language in social media posts to study the network dynamics of depression longitudinally.

ABSTRACT: Network theory of mental illness posits that causal interactions between symptoms give rise to mental health disorders. Increasing evidence suggests that depression network connectivity may be a risk factor for transitioning and sustaining a depressive state. Here we analysed social media (Twitter) data from 946 participants who retrospectively self-reported the dates of any depressive episodes in the past 12 months and current depressive symptom severity. We construct personalised, within-subject, networks based on depression-related linguistic features. We show an association existed between current depression severity and 8 out of 9 text features examined. Individuals with greater depression severity had higher overall network connectivity between depression-relevant linguistic features

SUBMITTER: Kelley SW 

PROVIDER: S-EPMC8847554 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature