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

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  • Sean W. Kelley

    (Trinity College Dublin
    Trinity College Dublin)

  • Claire M. Gillan

    (Trinity College Dublin
    Trinity College Dublin
    Trinity College Dublin)

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.

Suggested Citation

  • Sean W. Kelley & Claire M. Gillan, 2022. "Using language in social media posts to study the network dynamics of depression longitudinally," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28513-3
    DOI: 10.1038/s41467-022-28513-3
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    References listed on IDEAS

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    1. Krishna C. Bathina & Marijn ten Thij & Lorenzo Lorenzo-Luaces & Lauren A. Rutter & Johan Bollen, 2021. "Individuals with depression express more distorted thinking on social media," Nature Human Behaviour, Nature, vol. 5(4), pages 458-466, April.
    2. Maarten Bak & Marjan Drukker & Laila Hasmi & Jim van Os, 2016. "An n=1 Clinical Network Analysis of Symptoms and Treatment in Psychosis," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-15, September.
    3. Marten Scheffer & Jordi Bascompte & William A. Brock & Victor Brovkin & Stephen R. Carpenter & Vasilis Dakos & Hermann Held & Egbert H. van Nes & Max Rietkerk & George Sugihara, 2009. "Early-warning signals for critical transitions," Nature, Nature, vol. 461(7260), pages 53-59, September.
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    Cited by:

    1. Denny Borsboom, 2022. "Possible Futures for Network Psychometrics," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 253-265, March.

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