Skip to main content
Book cover

International Conference on Social Informatics

SocInfo 2020: Social Informatics pp 352–363Cite as

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

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

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.

Keywords

  • Adolescents
  • Depression
  • Psychological well-being
  • Digital traces
  • Validity
  • Sleep
  • Social networks
  • Social media

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-60975-7_26
  • Chapter length: 12 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   84.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-60975-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   109.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.

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.

References

  1. Fan, X., Miller, B.C., Park, K., Winward, B.W., Christensen, M., Grotevant, H.D.: An exploratory study about inaccuracy and invalidity in adolescent self-report surveys. Field Methods 18(3), 223–244 (2006). https://doi.org/10.1177/152822X06289161

    CrossRef  Google Scholar 

  2. Robinson-Cimpian, J.P.: Inaccurate estimation of disparities due to mischievous responders: several suggestions to assess conclusions. Educ. Res. 43(4), 171–185 (2014). https://doi.org/10.3102/0013189X14534297

    CrossRef  Google Scholar 

  3. Balazs, J., et al.: P-259-Prevalence of adolescent depression in Europe. Eur. Psychiatry 27, 1 (2012). https://www.sciencedirect.com/science/article/abs/pii/S0924933812744267

  4. Keyes, K.M., Gary, D., O’Malley, P.M., Hamilton, A., Schulenberg, J.: Recent increases in depressive symptoms among US adolescents: trends from 1991 to 2018. Soc. Psychiatry Psychiatric Epidemiol. 54(8), 987–996 (2019). https://doi.org/10.1007/s00127-019-01697-8

    CrossRef  Google Scholar 

  5. Ghandeharioun, A., et al.: Objective assessment of depressive symptoms with machine learning and wearable sensors data. In: 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII), pp. 325–332 (2017). http://doi.org/10.1109/ACII.2017.8273620

  6. Mohr, D.C., Zhang, M., Schueller, S.M.: Personal sensing: understanding mental health using ubiquitous sensors and machine learning. Ann. Rev. Clin. Psychol. 13, 23–47 (2017). https://doi.org/10.1146/annurev-clinpsy-032816-044949

    CrossRef  Google Scholar 

  7. Place, S., et al.: Behavioral indicators on a mobile sensing platform predict clinically validated psychiatric symptoms of mood and anxiety disorders. J. Med. Internet Res. 19(3), e75 (2017). https://doi.org/10.2196/jmir.6678. 1–9

    CrossRef  Google Scholar 

  8. Saeb, S., Lattie, E.G., Schueller, S.M., Kording, K.P., Mohr, D.C.: The relationship between mobile phone location sensor data and depressive symptom severity. PeerJ 4, e2537 (2016). https://doi.org/10.7717/peerj.2537. 1–15

    CrossRef  Google Scholar 

  9. De Choudhury, M., Gamon, M., Counts, S., Horvitz, E.: Predicting depression via social media. In: Seventh International AAAI Conference on Weblogs and Social Media (2013). https://www.aaai.org/ocs/index.php/ICWSM/ICWSM13/paper/viewFile/6124/6351

  10. Eichstaedt, J.C., et al.: Facebook language predicts depression in medical records. Proc. Natl. Acad. Sci. 115(44), 11203–11208 (2018). https://doi.org/10.1073/pnas.1802331115

    CrossRef  Google Scholar 

  11. Tackman, A.M., et al.: Depression, negative emotionality, and self-referential language: a multi-lab, multi-measure, and multi-language-task research synthesis. J. Pers. Soc. Psychol. 116(5), 817 (2019). https://doi.org/10.1037/pspp0000187

    CrossRef  Google Scholar 

  12. Resnik, P., Garron, A., Resnik, R.: Using topic modeling to improve prediction of neuroticism and depression in college students. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1348–1353 (2013). https://www.aclweb.org/anthology/D13-1133.pdf

  13. Reece, A.G., Danforth, C.M.: Instagram photos reveal predictive markers of depression. EPJ Data Sci. 6, 1–12 (2017). https://doi.org/10.1140/epjds/s13688-017-0110-z

    CrossRef  Google Scholar 

  14. Bollen, J., Mao, H., Pepe, A.: Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In: Fifth International AAAI Conference on Weblogs and Social Media (2011). https://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/viewFile/2826/3237

  15. Garcia, D., Rimé, B.: Collective emotions and social resilience in the digital traces after a terrorist attack. Psychol. Sci. 30(4), 617–628 (2019). https://doi.org/10.1177/0956797619831964

    CrossRef  Google Scholar 

  16. Eagle, N., Pentland, A.S.: Reality mining: sensing complex social systems. Pers. Ubiquit. Comput. 10(4), 255–268 (2006). http://realitycommons.media.mit.edu/pdfs/realitymining_old.pdf

  17. Aharony, N., Pan, W., Ip, C., Khayal, I., Pentland, A.: Social fMRI: investigating and shaping social mechanisms in the real world. Pervasive Mob. Comput. 7(6), 643–659 (2011). https://doi.org/10.1016/j.pmcj.2011.09.004

    CrossRef  Google Scholar 

  18. Stopczynski, A., et al.: Measuring large-scale social networks with high resolution. PLoS One 9(4), e95978 (2014). https://doi.org/10.1371/journal.pone.0095978

    CrossRef  Google Scholar 

  19. Wang, R., et al.: StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 3–14 (2014). https://doi.org/10.1145/2632048.2632054

  20. Bai, Y., Xu, B., Ma, Y., Sun, G., Zhao, Y.: Will you have a good sleep tonight?: sleep quality prediction with mobile phone. In: Proceedings of the 7th International Conference on Body Area Networks, pp. 124–130 (2012). https://dl.acm.org/doi/10.5555/2442691.2442720

  21. Sathyanarayana, A., et al.: Sleep quality prediction from wearable data using deep learning. JMIR mHealth uHealth 4(4), e125 (2016). https://doi.org/10.2196/mhealth.6562

    CrossRef  Google Scholar 

  22. Smarr, B.L., Schirmer, A.E.: 3.4 million real-world learning management system logins reveal the majority of students experience social jet lag correlated with decreased performance. Sci. Rep. 8, 1–9 (2018). https://doi.org/10.1038/s41598-018-23044-8

    CrossRef  Google Scholar 

  23. Fergusson, D.M., Wanner, B., Vitaro, F., Horwood, L.J., Swain-Campbell, N.: Deviant peer affiliations and depression: confounding or causation? J. Abnorm. Child Psychol. 31(6), 605–618 (2003). https://doi.org/10.1023/a:1026258106540

    CrossRef  Google Scholar 

  24. Kupersmidt, J.B., Coie, J.D.: Preadolescent peer status, aggression, and school adjustment as predictors of externalizing problems in adolescence. Child Dev. 61(5), 1350–1362 (1990). https://doi.org/10.1111/j.1467-8624.1990.tb02866.x

    CrossRef  Google Scholar 

  25. Zimmer-Gembeck, M.J.: Peer rejection, victimization, and relational self-system processes in adolescence: toward a transactional model of stress, coping, and developing sensitivities. Child Dev. Perspect. 10(2), 122–127 (2016). https://doi.org/10.1111/cdep.12174

    CrossRef  Google Scholar 

  26. Tang, J., Chang, S., Aggarwal, C., Liu, H.: Negative link prediction in social media. In: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, pp. 87–96 (2015). https://doi.org/10.1145/2684822.2685295

  27. Kroenke, K., Spitzer, R.L., Williams, J.B., Löwe, B.: The patient health questionnaire somatic, anxiety, and depressive symptom scales: a systematic review. Gener. Hospit. Psychiatry 32(4), 345–359 (2010). https://doi.org/10.1016/j.genhosppsych.2010.03.006

    CrossRef  Google Scholar 

  28. Andreas, J., Brunborg, G.S.: Depressive symptomatology among Norwegian adolescent boys and girls: the patient health Questionnaire-9 (PHQ-9) psychometric properties and correlates. Front. Psychol. 8, 887 (2017). https://doi.org/10.3389/fpsyg.2017.00887

    CrossRef  Google Scholar 

  29. Tsai, F.J., Huang, Y.H., Liu, H.C., Huang, K.Y., Huang, Y.H., Liu, S.I.: Patient health questionnaire for school-based depression screening among Chinese adolescents. Pediatrics 133, e402–e409 (2014). https://doi.org/10.1542/peds.2013-0204

    CrossRef  Google Scholar 

  30. Richardson, L.P., et al.: Evaluation of the Patient Health Questionnaire (PHQ-9) for detecting major depression among adolescents. Pediatrics 126, 1117–1123 (2010). https://doi.org/10.1542/peds.2010-0852

    CrossRef  Google Scholar 

  31. Fatiregun, A.A., Kumapayi, T.E.: Prevalence and correlates of depressive symptoms among in-school adolescents in a rural district in southwest Nigeria. J. Adolescents 37, 197–203 (2014). https://doi.org/10.1016/j.adolescence.2013.12.003

    CrossRef  Google Scholar 

  32. Ganguly, S., Samanta, M., Roy, P., Chatterjee, S., Kaplan, D.W., Basu, B.: Patient health questionnaire-9 as an effective tool for screening of depression among Indian adolescents. J. Adolescent Health 52(5), 546–551 (2013). https://doi.org/10.1016/j.jadohealth.2012.09.012

    CrossRef  Google Scholar 

  33. Tafoya, S.A., Aldrete-Cortez, V.: The interactive effect of positive mental health and subjective sleep quality on depressive symptoms in high school students. Behavioral Sleep Medicine 17(6), 818–826 (2019). https://doi.org/10.1080/15402002.2018.1518226

    CrossRef  Google Scholar 

  34. Tsehay, M., Necho, M., Mekonnen, W.: The role of adverse childhood experience on depression symptoms, prevalence, and severity among school going adolescents. Depress. Res. Treat. 2020, 1–9 (2020). https://doi.org/10.1155/2020/5951792

    CrossRef  Google Scholar 

  35. Leung, D.Y., Mak, Y.W., Leung, S.F., Chiang, V.C., Loke, A.Y.: Measurement invariances of the PHQ-9 across gender and age groups in Chinese adolescents. Asia-Pac. Psychiatry, e12381 (2020). https://doi.org/10.1111/appy.12381

  36. Spielberger, C.D., Sydeman, S.J., Owen, A.E., Marsh, B.J.: Measuring anxiety and anger with the State-Trait Anxiety Inventory (STAI) and the State-Trait Anger Expression Inventory (STAXI). In: Maruish, M.E. (ed.) The Use of Psychological Testing for Treatment Planning and Outcomes Assessment, pp. 993–1021. Lawrence Erlbaum Associates Publishers (1999)

    Google Scholar 

  37. Buysse, D.J., Reynolds, C.F., Monk, T.H., Berman, S.R., Kupfer, D.J.: The Pittsburgh Sleep Quality Index (PSQI): a new instrument for psychiatric research and practice. Psychiatry Res. 28(2), 193–213 (1989). https://doi.org/10.1016/0165-1781(89)90047-4

    CrossRef  Google Scholar 

  38. Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., Kappas, A.: Sentiment strength detection in short informal text. J. Am. Soc. Inf. Sci. Technol. 61(12), 2544–2558 (2010). https://doi.org/10.1002/asi.21416

    CrossRef  Google Scholar 

  39. Kern, M.L., et al.: Gaining insights from social media language: methodologies and challenges. Psychol. Methods 21(4), 507–525 (2016). https://doi.org/10.1037/met0000091

    CrossRef  Google Scholar 

  40. Jaidka, K., Guntuku, S.C., Buffone, A., Schwartz, H.A., Ungar, L.H.: Facebook vs. Twitter: cross-platform differences in self-disclosure and trait prediction. In: Proceedings of the Twelfth International AAAI Conference on Web and Social Media, pp. 141–150 (2018). https://aaai.org/ocs/index.php/ICWSM/ICWSM18/paper/view/17882

  41. Jungherr, A.: Normalizing digital trace data. In: Stroud, N.J., McGregor, S. (eds.) Digital Discussions: How Big Data Informs Political Communication. Routledge (2018). https://doi.org/10.4324/9781351209434

Download references

Acknowledgements

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

Author information

Authors and Affiliations

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

    Elizaveta Sivak & Ivan Smirnov

Authors
  1. Elizaveta Sivak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ivan Smirnov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ivan Smirnov .

Editor information

Editors and Affiliations

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

    Samin Aref

  2. University of Sheffield, Sheffield, UK

    Prof. Kalina Bontcheva

  3. King’s College London, London, UK

    Marco Braghieri

  4. Umeå University, Umeå, Sweden

    Prof. Dr. Frank Dignum

  5. ISTI-CNR, Pisa, Italy

    Prof. Fosca Giannotti

  6. University of Pisa, Pisa, Italy

    Francesco Grisolia

  7. University of Pisa, Pisa, Italy

    Dino Pedreschi

Rights and permissions

Reprints and Permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Sivak, E., Smirnov, I. (2020). Measuring Adolescents’ Well-Being: Correspondence of Naïve Digital Traces to Survey Data. In: , 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

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60974-0

  • Online ISBN: 978-3-030-60975-7

  • eBook Packages: Computer ScienceComputer Science (R0)