Background: Network theory of mental illness posits that causal interactions between symptoms give rise to mental health disorders. This is thought to occur because positive feedback loops between symptoms trigger cascades of further symptom activation. Increasing evidence suggests that depression network connectivity is therefore a risk factor for transitioning and sustaining a depressive state. However, much of the evidence comes from cross-sectional studies that estimate networks across groups, rather than within individuals. We used a novel method to construct personalised depression-relevant networks from social media data to test the hypothesis that network connectivity is linked to depression severity and increases during a depressive episode.
Methods: We analysed Twitter data from 946 participants who retrospectively reported the dates of any depressive episodes they experienced in the past 12 months and self-reported current depressive symptom severity. Daily Tweets were subjected to textual analysis, which allowed us to construct personalised, within-subject, depression networks, based on 9 a priori text features previously associated with depression severity. We tested for associations between network connectivity and current depression severity and, in participants who experienced a depressive episode in the past year, we tested if connectivity increased during the dates of a self-reported episode (N = 286).
Results: Significant bivariate associations were found between current depression severity and 8/9 of the text features examined. In line with our hypothesis, individuals with greater depression severity had a significantly higher overall network connectivity between these features than those with lesser severity (β = 0.008, SE = 0.003, p = 0.002). Importantly, we observed within-subject changes in overall network connectivity associated with the dates of a self-reported depressive episode (β = 0.03, SE = 0.009, p = 0.005).
Conclusions: The connectivity within personalized depression networks changes dynamically with changes in current depression symptoms. Social media data provides a fruitful, albeit noisy, source of data to test key within-subject predictions of network theory.