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

• Published in: Nature communications Vol. 13; no. 1; pp. 870 - 11
• Main Authors: Kelley, Sean W., Gillan, Claire M.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 15.02.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/2397; 631/477/2811; Adult; Connectivity; Customization; Depression - physiopathology; Depressive Disorder, Major - physiopathology; Digital media; Female; Humanities and Social Sciences; Humans; Language; Linguistics; Linguistics - statistics & numerical data; Male; Mental depression; Mental disorders; Mental health; multidisciplinary; Risk analysis; Risk factors; Science; Science (multidisciplinary); Self Report - statistics & numerical data; Severity of Illness Index; Signs and symptoms; Social media; Social Media - statistics & numerical data; Social networks
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-28513-3

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 than those with lesser severity. We observed within-subject changes in overall network connectivity 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. Depression network connectivity is a risk factor for developing depression. Here the authors show personalised networks of depression-related linguistic features were linked to network connectivity within a self-reported depressive episode.