Predicting Subjective Well-Being by Smartphone Usage Behaviors

Yusong Gao, He Li, Tingshao Zhu

2014

Abstract

Subjective Well-Being (SWB) refers to how people experience the quality of their lives, thus to acquire people’s SWB levels timely and effectively is very important. Self-report and interviewing are mostly used techniques for assessing SWB, but cannot be done in time. This study aims to predict one’s SWB levels by smartphone usage behaviours. We collect users’ smartphone usage and self-reported subjective well-being, and found that several usage behaviours correlate with SWB, especially for females. For example, smartphone users with higher SWB scores tend to use more communicating apps, play more games and read more, but take fewer photos. Based on these findings, we trained a predicting model of user’s SWB based on smartphone usage behaviours, and the accuracy is up to 62%. The result indicates that SWB can be identified based on smartphone usage fairly well.

References

  1. Angner, E., 2010. Subjective well-being. The Journal of Socio-Economics, 39(3), 361-368.
  2. Diener, E., 1994. Assessing subjective well-being: Progress and opportunities. Social indicators research, 31, 103--157.
  3. Diener, E., 2000. Subjective well-being: The science of happiness and a proposal for a national index. American psychologist, 55(1), 34.
  4. Lyubomirsky, S., 2001. Why are some people happier than others? The role of cognitive and motivational processes in well-being. American Psychologist,56(3), 239.
  5. Billieux, J., Van der Linden, M., & Rochat, L., 2008. The role of impulsivity in actual and problematic use of the mobile phone. Applied Cognitive Psychology, 22(9), 1195-1210.
  6. Chittaranjan, G., Blom, J., & Gatica-Perez, D., 2011. Mining large-scale smartphone data for personality studies. Personal and Ubiquitous Computing, 17(3), 433-450.
  7. Ehrenberg, A., Juckes, S., White, K. M., & Walsh, S. P., 2008. Personality and self-esteem as predictors of young people's technology use. Cyberpsychol Behav, 11(6), 739-741.
  8. Soikkeli, T., Karikoski, J., & Hammainen, H., 2011. Diversity and End User Context in Smartphone Usage Sessions. 7-12.
  9. Frey, B. S., 2011. Happy people live longer. Science, 331(6017), 542-543.
  10. Coen A, Dai L, Herzig S, et al., 2002. The Analysis: Investigation of the cellular phone industry [J]. Retrieved October, 2002, 21: 2006.
  11. Suh, B., & Han, I., 2003. Effect of trust on customer acceptance of Internet banking. Electronic Commerce research and applications, 1(3), 247-263.
  12. Back MD, Stopfer JM, Vazire S, Gaddis S, Schmukle SC, Egloff B, Gosling SD., 2010. Facebook profiles reflect actual personality, not self-idealization. Psychol Sci 21:372-374
  13. Biel, J. I., Aran, O., & Gatica-Perez, D., 2011. You Are Known by How You Vlog: Personality Impressions and Nonverbal Behavior in YouTube. In ICWSM.
  14. Counts, S., & Stecher, K. B., 2009. Self-Presentation of Personality During Online Profile Creation. In ICWSM.
  15. Stecher, K. B., & Counts, S., 2008. Spontaneous Inference of Personality Traits and Effects on Memory for Online Profiles. In ICWSM.
  16. Yeo, T. D., 2010. Modeling Personality Influences on YouTube Usage. In ICWSM.
  17. Butt, S., & Phillips, J. G., 2008. Personality and self reported mobile phone use. Computers in Human Behavior, 24(2), 346-360.
  18. Gross, E. F., Juvonen, J., & Gable, S. L., 2002. Internet use and well-being in adolescence. Journal of Social Issues, 58(1), 75-90.
  19. LiKamWa R., 2013. MoodScope: Building a Mood Sensor from Smartphone Use Patterns. In ACM.
  20. Diener, E. D., Emmons, R. A., Larsen, R. J., & Griffin, S., 1985. The satisfaction with life scale. Journal of personality assessment, 49(1), 71-75.
  21. Diener, E., Suh, E. M., Lucas, R. E., & Smith, H. L., 1999. Subjective well-being: Three decades of progress. Psychological bulletin, 125(2), 276.
  22. Kwak, N., & Choi, C. H., 2002. Input feature selection for classification problems. Neural Networks, IEEE Transactions on, 13(1), 143-159.
  23. Witten, I. H., & Frank, E., 2005. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
  24. Wang, Y., & Witten, I. H., 1999. Pace regression.
  25. Li, A., Li, H., Guo, R., & Zhu, T., 2013. MobileSens: A Ubiquitous Psychological Laboratory based on Mobile Device. International Journal of Cyber Behavior, Psychology and Learning (IJCBPL), 3(2), 47-55.
  26. Guo, R., Zhu, T., Wang, Y., & Xu, X., 2011. MobileSens: A framework of behavior logger on Andriod mobile device. In Pervasive Computing and Applications (ICPCA), 2011 6th International Conference on. IEEE, 2011: 281-286.
  27. Workshop on New Computationally-Enabled Theoretical Models to Support Health Behavior Change and Maintenance. (2012). Realizing effective behavioural management of health. Available: http://www.behaviorchange.be/
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Paper Citation


in Harvard Style

Gao Y., Li H. and Zhu T. (2014). Predicting Subjective Well-Being by Smartphone Usage Behaviors . In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2014) ISBN 978-989-758-010-9, pages 317-322. DOI: 10.5220/0004800203170322


in Bibtex Style

@conference{healthinf14,
author={Yusong Gao and He Li and Tingshao Zhu},
title={Predicting Subjective Well-Being by Smartphone Usage Behaviors},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2014)},
year={2014},
pages={317-322},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004800203170322},
isbn={978-989-758-010-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2014)
TI - Predicting Subjective Well-Being by Smartphone Usage Behaviors
SN - 978-989-758-010-9
AU - Gao Y.
AU - Li H.
AU - Zhu T.
PY - 2014
SP - 317
EP - 322
DO - 10.5220/0004800203170322