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Identifying Depression-Related Behavior on Facebook—An Experimental Study

Author

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  • Zoltán Kmetty

    (Computational Social Science—Research Center for Educational and Network Studies, Sociology Department and Centre for Social Sciences, Faculty of Social Sciences, Eötvös Loránd University, 1117 Budapest, Hungary)

  • Károly Bozsonyi

    (Institute of Social and Communication Sciences, Faculty of Social Sciences, Károli Gáspár University of the Reformed Church in Hungary, 1091 Budapest, Hungary)

Abstract

Depression is one of the major mental health problems in the world and the leading cause of disability worldwide. As people leave more and more digital traces in the online world, it becomes possible to detect depression-related behavior based on people’s online activities. We use a novel Facebook study to identify possible non-textual elements of depression-related behavior in a social media environment. This study focuses on the relationship between depression and the volume and composition of Facebook friendship networks and the volume and temporal variability of Facebook activities. We also tried to establish a link between depression and the interest categories of the participants. The significant predictors were partly different for cognitive-affective depression and somatic depression. Earlier studies found that depressed people have a smaller online social network. We found the same pattern in the case of cognitive-affective depression. We also found that they posted less in others’ timelines, but we did not find that they posted more in their own timeline. Our study was the first to use the Facebook ads interest data to predict depression. Those who were classified into the less interest category by Facebook had higher depression levels on both scales.

Suggested Citation

  • Zoltán Kmetty & Károly Bozsonyi, 2022. "Identifying Depression-Related Behavior on Facebook—An Experimental Study," Social Sciences, MDPI, vol. 11(3), pages 1-19, March.
    • Handle: RePEc:gam:jscscx:v:11:y:2022:i:3:p:135-:d:773655
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    References listed on IDEAS

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    1. Ulrich S Tran & Rita Andel & Thomas Niederkrotenthaler & Benedikt Till & Vladeta Ajdacic-Gross & Martin Voracek, 2017. "Low validity of Google Trends for behavioral forecasting of national suicide rates," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-26, August.
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