Personal and Ubiquitous Computing

, Volume 17, Issue 3, pp 433–450 | Cite as

Mining large-scale smartphone data for personality studies

  • Gokul Chittaranjan
  • Jan Blom
  • Daniel Gatica-PerezEmail author
Original Article


In this paper, we investigate the relationship between automatically extracted behavioral characteristics derived from rich smartphone data and self-reported Big-Five personality traits (extraversion, agreeableness, conscientiousness, emotional stability and openness to experience). Our data stem from smartphones of 117 Nokia N95 smartphone users, collected over a continuous period of 17 months in Switzerland. From the analysis, we show that several aggregated features obtained from smartphone usage data can be indicators of the Big-Five traits. Next, we describe a machine learning method to detect the personality trait of a user based on smartphone usage. Finally, we study the benefits of using gender-specific models for this task. Apart from a psychological viewpoint, this study facilitates further research on the automated classification and usage of personality traits for personalizing services on smartphones.


Smartphones Big-Five Personality Lausanne data collection campaign 



This work was funded by the SNSF project “Sensing and Analyzing Organizational Nonverbal Behavior" (SONVB) and by Nokia Research Center (NRC) Lausanne. We thank Juha K. Laurila (NRC) and Trinh-Minh-Tri Do (Idiap) for valuable discussions.


  1. 1.
    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 21:372–374CrossRefGoogle Scholar
  2. 2.
    Bianchi A, Phillips J (2005) Psychological predictors of problem mobile phone use. CyberPsychol and Behav 8:39–51CrossRefGoogle Scholar
  3. 3.
    Biel J, Aran O, Gatica-Perez D (2011) You are known by how you vlog: Personality impressions and nonverbal behavior in youtube. In: Proceedings of international AAAI conference on weblogs and social media (ICWSM)Google Scholar
  4. 4.
    Brinkman WP, Fine N (2005) Towards customized emotional design: an explorative study of user personality and user interface skin preferences. In: Proceedings of annual conference on european association of cognitive ergonomics (EACE)Google Scholar
  5. 5.
    Butt S, Phillips JG (2008) Personality and self reported mobile phone use. Comput Hum Behav 24:346–360CrossRefGoogle Scholar
  6. 6.
    Chittaranjan G, Blom J, Gatica-Perez D (2011) Who’s who with big-five: Analyzing and classifying personality traits with smartphones. In: Proceedings of the 15th international symposium on wearable computers (ISWC), San Francisco, USAGoogle Scholar
  7. 7.
    Coppersmith S (1959) A method for determining types of self-esteem. J Abnorm Soc Psychol 59:87–94CrossRefGoogle Scholar
  8. 8.
    Costa PT, J, McCrae RR (1992) Revised NEO personality inventory (NEO-PI-R) and NEO five- factor inventory (NEO-FFI) professional manual. Psychological assessment inventoriesGoogle Scholar
  9. 9.
    Counts S, Stecher K (2009) Self-presentation of personality during online profile creation. In: Proceedings of international AAAI conference on weblogs and social media (ICWSM)Google Scholar
  10. 10.
    Do T, Gatica-Perez D (2010) By their apps you shall understand them: mining large-scale patterns of mobile phone usage. In: Proceedings of international conference on mobile and ubiquitous multimedia (MUM)Google Scholar
  11. 11.
    Eagle N, Pentland A (2009) Eigenbehaviors: identifying structure in routine. Behav Ecol Sociobiol 63:1057–1066CrossRefGoogle Scholar
  12. 12.
    Farrahi K, Gatica-Perez D (2010) Probabilistic mining of socio-geographic routines from mobile phone data. IEEE J Sel Top Signal Process 4:746–755CrossRefGoogle Scholar
  13. 13.
    Gosling SD, Rentfrow PJ, Swann W (2003) A very brief measure of the big-five personality domains. J Res Pers 37:504–528CrossRefGoogle Scholar
  14. 14.
    Graham L, Gosling S (2011) Can the ambiance of a place be determined by the user profiles of the people who visit it? In: Proceedings of international AAAI conference on weblogs and social media (ICWSM)Google Scholar
  15. 15.
    I.c.t. statistics. (Accessed 9 Feb 2011)
  16. 16.
    Karsvall A (2002) Personality preferences in graphical interface design. In: Proceedings of the second nordic conference on human-computer interaction (NordiCHI)Google Scholar
  17. 17.
    Kiukkonen N, Blom J, Dousse O, Gatica-Perez D, Laurila J (2010) Towards rich mobile phone datasets: Lausanne data collection campaign. In: Proceedings of ACM international conference on pervasive services (ICPS)Google Scholar
  18. 18.
    Kraut R, Patterson M, Lundmark V, Kiesler S, Mukapadhyay T, Scherlis W (1998) Internet-paradox: a social technology that reduces social involvement and psychological well being?. Am Psychol 53:1017–1031CrossRefGoogle Scholar
  19. 19.
    Mairesse F, Walker M (2006) Automatic recognition of personality in conversation. In: Proceedings of human language technology conference of the NAACL, companion volume: short papers (NAACL-Short)Google Scholar
  20. 20.
    Mairesse F, Walker M, Mehl MR, Moore RK (2007) Using linguistic cues for the automatic recognition of personality in conversation and text. J Artif Intell Res 30:457–500zbMATHGoogle Scholar
  21. 21.
    McCrae RR, John OP (1992) An introduction to the five-factor model and its applications. J Pers 60:175–215CrossRefGoogle Scholar
  22. 22.
    Oliveira R, Karatzoglou A, Concejero P, Armenta A, Olivier N (2011) Towards a psychographic user model from mobile phone usage. In: Proceedings ACM CHI conference on human factors in computing systems (CHI) work in progress (WIP)Google Scholar
  23. 23.
    Phillips JG, Butt S, Blaszczynski A (2006) Personality and self-reported use of mobile phones for games. Cyberpsychol and Behav 9:753–758CrossRefGoogle Scholar
  24. 24.
    Pianesi F, Mana N, Cappelletti A, Lepri B, Zancanaro M (2008) Multimodal recognition of personality traits in social interactions. In: Proceedings of International conf. on multimodal interfaces (ICMI)Google Scholar
  25. 25.
    Poschl S, Doring N (2007) Integration and ubiquity. Towards a philosophy of telecommunications convergence, chap. Personality and the mobile phone: character-based differences of usage and attitudes towards mobile communication, pp. 161–168. Vienna, Passagen VerlagGoogle Scholar
  26. 26.
    Romero JJ (2011) Top 11 technologies of the decade. IEEE Spectrum, 24–27Google Scholar
  27. 27.
    Sonnenburg S, Raetsch G, Henschel S, Widmer C, Behr J, Zien A, Bona FD, Binder A, Gehl C, Franc V (2010) The shogun machine learning toolbox. J Mach Learn Res 11:1799–1802zbMATHGoogle Scholar
  28. 28.
    Stecher K, Counts S (2008) Spontaneous inference of personality traits and effects on memory for online profiles. In: Proceedings of international AAAI conference on weblogs and social media (ICWSM)Google Scholar
  29. 29.
    Tabachnick B, Fidell L (2000) Using multivariate statistics, 4th edn. Allyn & Bacon, BostonGoogle Scholar
  30. 30.
    Appendix ii: Normative data for the ten-item personality inventory (tipi): Self-reported data. (Accessed 9 Feb 2010)
  31. 31.
    Verkasalo H, Lpez-Nicols C, Molina-Castillo FJ, Bouwman H (2010) Analysis of users and non-users of smartphone applications. Telemat Informat 27:242–255CrossRefGoogle Scholar
  32. 32.
    Yeo T (2010) Modeling personality influences on youtube usage. In: Proceedings of international AAAI conference on weblogs and social media (ICWSM)Google Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Gokul Chittaranjan
    • 1
    • 2
  • Jan Blom
    • 3
  • Daniel Gatica-Perez
    • 1
    • 2
    Email author
  1. 1.Idiap Research InstituteCentre du ParcMartignySwitzerland
  2. 2.École Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
  3. 3.Nokia Research Center LausannePSE-C, EPFLLausanneSwitzerland

Personalised recommendations