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Individuals with depression express more distorted thinking on social media

Nature Human Behaviour volume 5, pages 458–466 (2021)Cite this article

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Abstract

Depression is a leading cause of disability worldwide, but is often underdiagnosed and undertreated. Cognitive behavioural therapy holds that individuals with depression exhibit distorted modes of thinking, that is, cognitive distortions, that can negatively affect their emotions and motivation. Here, we show that the language of individuals with a self-reported diagnosis of depression on social media is characterized by higher levels of distorted thinking compared with a random sample. This effect is specific to the distorted nature of the expression and cannot be explained by the presence of specific topics, sentiment or first-person pronouns. This study identifies online language patterns that are indicative of depression-related distorted thinking. We caution that any future applications of this research should carefully consider ethical and data privacy issues.

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Fig. 1: Cohort of individuals with depression.
Fig. 2: Within-individual CDS prevalence and between-cohort PR values.
Fig. 3: CDS and tweet sentiment scores (VADER).

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Data availability

All data used in this study are available in deidentified form in a dedicated and open GitHub repository (https://github.com/mctenthij/CDS_paper). Any additional information with respect to the data used in this study will be made available from the corresponding author upon reasonable request, provided this information can be made available in deidentified form. Any additional data and information are available from the corresponding author on reasonable request.

Code availability

The code and related data of this study are freely available at GitHub (https://github.com/mctenthij/CDS_paper) enabling reproduction.

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Acknowledgements

We thank L. M. Rocha for his feedback on the general methodology and terminology, as well as K. Dobson, R. DeRubeis, C. Webb, S. Hoffman, N. Kazantzis, J. Garber and R. Jarrett for their feedback on the content of our list of CDS. J.B. thanks NSF grant SMA/SME1636636, COVID-19 funding from IU’s Office of the Vice President for Research, The Urban Mental Health institute at the University of Amsterdam, Wageningen University and Research, and the ISI Foundation for their support. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN, USA

    Krishna C. Bathina, Marijn ten Thij & Johan Bollen

  2. Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, USA

    Lorenzo Lorenzo-Luaces & Lauren A. Rutter

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  1. Krishna C. Bathina
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  2. Marijn ten Thij
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  3. Lorenzo Lorenzo-Luaces
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  4. Lauren A. Rutter
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  5. Johan Bollen
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Contributions

M.t.T. and J.B. conceptualized the analysis; M.t.T. and J.B. designed the methodology; L.A.R., L.L.-L. and J.B. constructed the CDS lexicon; K.C.B. and M.t.T. constructed the datasets; K.C.B., M.t.T. and J.B. performed data analysis; and K.C.B., M.t.T., L.A.R., L.L.-L. and J.B. wrote the manuscript.

Corresponding author

Correspondence to Johan Bollen.

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Competing interests

L.L.-L. received an honorarium for consulting from Happify, Inc. in September 2020. Happify, Inc. creates behavioural change technologies (for example, apps) for mental health. Happify had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The remaining authors declare no competing interests.

Additional information

Peer review information Nature Human Behaviour thanks Glen Coppersmith, Christopher Danforth and David Dozois for their contribution to the peer review of this work. Primary Handling Editor: Jamie Horder.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–4, Supplementary Tables 1–4, and an overview of exact CDS, the time-matched control and the control for the presence of hyperlinks.

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Bathina, K.C., ten Thij, M., Lorenzo-Luaces, L. et al. Individuals with depression express more distorted thinking on social media. Nat Hum Behav 5, 458–466 (2021). https://doi.org/10.1038/s41562-021-01050-7

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