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

Sean W. Kelley () and Claire M. Gillan ()
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Sean W. Kelley: Trinity College Dublin
Claire M. Gillan: Trinity College Dublin

Nature Communications, 2022, vol. 13, issue 1, 1-11

Abstract: 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 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.

Date: 2022
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DOI: 10.1038/s41467-022-28513-3

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