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Measuring Adolescents’ Well-Being: Correspondence of Naïve Digital Traces to Survey Data

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Social Informatics (SocInfo 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12467))

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Abstract

Digital traces are often used as a substitute for survey data. However, it is unclear whether and how digital traces actually correspond to the survey-based traits they purport to measure. This paper examines correlations between self-reports and digital trace proxies of depression, anxiety, mood, social integration and sleep among high school students. The study is based on a small but rich multilayer data set (N = 144). The data set contains mood and sleep measures, assessed daily over a 4-month period, along with survey measures at two points in time and information about online activity from VK, the most popular social networking site in Russia. Our analysis indicates that 1) the sentiments expressed in social media posts are correlated with depression; namely, adolescents with more severe symptoms of depression write more negative posts, 2) late-night posting indicates less sleep and poorer sleep quality, and 3) students who were nominated less often as somebody’s friend in the survey have fewer friends on VK and their posts receive fewer “likes.” However, these correlations are generally weak. These results demonstrate that digital traces can serve as useful supplements to, rather than substitutes for, survey data in studies on adolescents’ well-being. These estimates of correlations between survey and digital trace data could provide useful guidelines for future research on the topic.

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Notes

  1. 1.

    Due to the sensitive nature of the data, the files are encrypted. The password to decrypt the files will be sent upon request. https://osf.io/b57rp/?view_only=f872ea8355cb4d818683e29967282a23.

  2. 2.

    https://www.phqscreeners.com/.

  3. 3.

    https://www.sleep.pitt.edu/instruments/.

  4. 4.

    http://sentistrength.wlv.ac.uk/.

  5. 5.

    http://sentistrength.wlv.ac.uk/#Non-English.

  6. 6.

    We used a bootstrap approach for hierarchically structured data to compute the standard errors of the mean for positive sentiment and mood on each day of the week as well as 90% confidence intervals for the mean positive sentiment and mood on weekdays and weekends as a correction for repeated measures within the same participant. We implemented a bootstrap approach with replacement on the level of measurements http://biostat.mc.vanderbilt.edu/wiki/Main/HowToBootstrapCorrelatedData.

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Acknowledgements

This work was supported by a grant from the Russian Science Foundation (project №19-18-00271).

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Authors and Affiliations

  1. Institute of Education, National Research University Higher School of Economics, Myasnitskaya ul., 20, Moscow, 101000, Russia

    Elizaveta Sivak & Ivan Smirnov

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  1. Elizaveta Sivak
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  2. Ivan Smirnov
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Correspondence to Ivan Smirnov .

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Editors and Affiliations

  1. Max Planck Institute for Demographic Research, Rostock, Germany

    Samin Aref

  2. University of Sheffield, Sheffield, UK

    Kalina Bontcheva

  3. King’s College London, London, UK

    Marco Braghieri

  4. Umeå University, Umeå, Sweden

    Frank Dignum

  5. ISTI-CNR, Pisa, Italy

    Fosca Giannotti

  6. University of Pisa, Pisa, Italy

    Francesco Grisolia

  7. University of Pisa, Pisa, Italy

    Dino Pedreschi

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Sivak, E., Smirnov, I. (2020). Measuring Adolescents’ Well-Being: Correspondence of Naïve Digital Traces to Survey Data. In: Aref, S., et al. Social Informatics. SocInfo 2020. Lecture Notes in Computer Science(), vol 12467. Springer, Cham. https://doi.org/10.1007/978-3-030-60975-7_26

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  • DOI: https://doi.org/10.1007/978-3-030-60975-7_26

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