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. This study analyzed 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. Personalized, within-subject networks were constructed based on depression-related linguistic features. The study found an association between current depression severity and 8 out of 9 text features examined. Individuals with greater depression severity had higher overall network connectivity. Within-subject changes in overall network connectivity were observed associated with the dates of a self-reported depressive episode. The connectivity within personalized networks of depression-associated linguistic features may change dynamically with changes in current depression symptoms.

Sean W. Kelley, Claire M. Gillan