Demographics of mobile app usage: long-term analysis of mobile app usage

Abstract

In the past decade, mobile app usage has played an important role in our daily life. Existing studies have shown that app usage is intrinsically linked with, among others, demographics, social and economic factors. However, due to data limitations, most of these studies have a short time span and treat users in a static manner. To date, no study has shown whether changes in socioeconomic status or other demographics are reflected in long-term app usage behavior. In this paper, we contribute by presenting the first ever long-term study of individual mobile app usage dynamics and how app usage behavior of individuals is influenced by changes in socioeconomic demographic factors over time. Through a novel app dataset we collected, from which we extracted records of 1608 long-term users with more than 3-year app usage and their detailed socioeconomic attributes, we verify the stable correlation between user app usage and user socioeconomic attributes over time and identify a number of representative app usage patterns in connection with specific user attributes. On the basis, we analyze the long-term app usage dynamics and reveal that there is significant evolution in long-term app usage that 60–70% of users change their app usage patterns during the duration of more than 3 years. We further discover a variety of app pattern change modes and demonstrate that the long-term app usage behavior change reflects corresponding transition in socioeconomic attributes, such as change of civil status, family size, transition in job or economic status.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

References

  1. Abrahamse, W., Steg, L.: How do socio-demographic and psychological factors relate to households direct and indirect energy use and savings? J. Econ. Psychol. 30(5), 711–720 (2009)

    Article  Google Scholar 

  2. Aggarwal, V., Halepovic, E., Pang, J., Venkataraman, S., Yan, H.: Prometheus: toward quality-of-experience estimation for mobile apps from passive network measurements. In: Proceedings of the 15th ACM Workshop on Mobile Computing Systems and Applications (HotMobile: ACM. Santa Barbara, California, US (2014)

  3. Almaatouq, A., Prieto-Castrillo, F., Pentland, A.: Mobile communication signatures of unemployment. In: International Conference on Social Informatics, Springer, pp 407–418 (2016)

  4. Althoff, T., Jindal, P., Leskovec, J.: Online actions with offline impact: How online social networks influence online and offline user behavior. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, ACM, pp 537–546 (2017)

  5. Blaszkiewicz, K., Blaszkiewicz, K., Blaszkiewicz, K., Markowetz, A.: Differentiating smartphone users by app usage. In: Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), ACM, pp 519–523 (2016)

  6. Cao, H., Chen, Z., Xu, F., Li, Y., Kostakos, V.: Revisitation in urban space vs. online: a comparison across pois, websites, and smartphone apps. Proc. ACM Interact. Mob. Wearable Ubiquit. Technol. 2(4), 156 (2018a)

    Google Scholar 

  7. Cao, H., Feng, J., Li, Y., Kostakos, V.: Uniqueness in the city: urban morphology and location privacy. Proc. ACM Interact. Mob. Wearable Ubiquit. Technol. 2(2), 1–20 (2018b)

    Google Scholar 

  8. Cao, H., Xu, F., Sankaranarayanan, J., Li, Y., Samet, H.: Habit2vec: trajectory semantic embedding for living pattern recognition in population. IEEE Trans. Mob. Comput. 19, 1096–1108 (2019)

    Article  Google Scholar 

  9. Coie, J.D., Dodge, K.A.: Continuities and changes in children’s social status: a five-year longitudinal study. Merrill-Palmer Q. (1982-), 1, 261–282 (1983)

    Google Scholar 

  10. Cooper, J.O., Heron, T.E., Heward, W.L., et al.: Applied Behavior Analysis. Pearson/Merrill-Prentice Hall, Upper Saddle River (2007)

    Google Scholar 

  11. Cortes, C., Vapnik, V.: Support vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

  12. Danescu-Niculescu-Mizil, C., West, R., Jurafsky, D., Leskovec, J., Potts, C.: No country for old members: User lifecycle and linguistic change in online communities. In: Proceedings of the 22nd international conference on World Wide Web, ACM, pp 307–318 (2013)

  13. Do, T.M.T., Blom, J., Gatica-Perez, D.: Smartphone usage in the wild: a large-scale analysis of applications and context. In: Proceedings of the 13th International Conference on Multimodal Interfaces (ICMI), pp 353–360 (2011)

  14. Dong, X., Jahani, E., Morales, A.J., Bozkaya, B., Lepri, B., Pentland, A.: Purchase patterns, socioeconomic status, and political inclination. In: International Conference on Computational Social Science (2016)

  15. Dror, G., Pelleg, D., Rokhlenko, O., Szpektor, I.: Churn prediction in new users of yahoo! answers. In: Proceedings of the 21st International Conference on World Wide Web, ACM, pp 829–834 (2012)

  16. Ducheneaut, N., Yee, N., Nickell, E., Moore, R.J.: The life and death of online gaming communities: a look at guilds in world of warcraft. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM, pp 839–848 (2007)

  17. Eagle, N., Pentland, A.S., Lazer, D.: Inferring friendship network structure by using mobile phone data. Proc. Natl. Acad. Sci. 106(36), 15274–15278 (2009)

    Article  Google Scholar 

  18. Falaki, H., Mahajan, R., Kandula, S., Lymberopoulos, D., Govindan, R., Estrin, D.: Diversity in smartphone usage. In: Proceedings of the 8th International Conference on Mobile Systems, Applications and Services (MobiSys), pp 179–194 (2010)

  19. Felbo, B., Sundsøy, P., Lehmann, S., de Montjoye, Y.A., et al.: Using deep learning to predict demographics from mobile phone metadata. In: International Conference on Representation Learning (ICLR) 2016 Workshop (2016)

  20. Ferreira, D., Goncalves, J., Kostakos, V., Barkhuus, L., Dey, A.K.: Contextual experience sampling of mobile application micro-usage. In: Proceedings of the 16th International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI), pp 91–100 (2014)

  21. Gordon-Larsen, P., Nelson, M.C., Popkin, B.M.: Longitudinal physical activity and sedentary behavior trends: adolescence to adulthood. Am. J. Prev. Med. 27(4), 277–283 (2004)

    Article  Google Scholar 

  22. Hata, K., Krishna, R., Fei-Fei, L., Bernstein, M.: A glimpse far into the future: Understanding long-term crowd worker quality. In: CSCW: Computer-Supported Cooperative Work and Social Computing (2017)

  23. Hochberg, Y., Benjamini, Y.: More powerful procedures for multiple significance testing. Stat. Med. 9(7), 811–818 (1990)

    Article  Google Scholar 

  24. Huang, K., Zhang, C., Ma, X., Chen, G.: Predicting mobile application usage using contextual information. In: Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), pp 1059–1065 (2012)

  25. Jessor, R., Jessor, S.L.: Problem Behavior and Psychosocial Development: A Longitudinal Study of Youth. Academic Press, New York (1977)

    Google Scholar 

  26. Jones, S.L., Ferreira, D., Hosio, S., Goncalves, J., Kostakos, V.: Revisitation analysis of smartphone app use. In: Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), ACM, pp 1197–1208 (2015)

  27. Kalmus, V., Realo, A., Siibak, A.: Motives for internet use and their relationships with personality traits and socio-demographic factors. Trames J. Hum. Soc. Sci. 15(4), 385 (2011)

    Google Scholar 

  28. Kang, S., Jung, J.: Mobile communication for human needs: a comparison of smartphone use between the US and Korea. Comput. Hum. Behav. 35, 376–387 (2014)

    Article  Google Scholar 

  29. Kent, J.T.: Information gain and a general measure of correlation. Biometrika 70(1), 163–173 (1983)

    MathSciNet  Article  Google Scholar 

  30. Kooti, F., Yang, H., Cha, M., Gummadi, K.P., Mason, W.A.: The emergence of conventions in online social networks. In: Sixth International AAAI Conference on Weblogs and Social Media (2012)

  31. Lenormand, M., Louail, T., Cantú-Ros, O.G., Picornell, M., Herranz, R., Arias, J.M., Barthelemy, M., San Miguel, M., Ramasco, J.J.: Influence of sociodemographic characteristics on human mobility. Sci. Rep. 5, 10075 (2015)

    Article  Google Scholar 

  32. Leroux, P., Roobroeck, K., Dhoedt, B., Demeester, P., Turck, F.D.: Mobile application usage prediction through context-based learning. J. Ambient Intell. Smart Environ. 5(2), 213–235 (2013)

    Article  Google Scholar 

  33. Li, H., Lu, X., Liu, X., Xie, T., Bian, K., Lin, F.X., Feng, F., Feng, F.: Characterizing smartphone usage patterns from millions of android users. In: Proceedings of the Conference on Internet Measurement Conference (IMC), pp 459–472 (2015)

  34. Li, T., Zhang, M., Cao, H., Li, Y., Tarkoma, S., Hui, P.: what apps did you use?: understanding the long-term evolution of mobile app usage. Proc. Web Conf. 2020, 66–76 (2020)

    Google Scholar 

  35. Liao, ZhungXun, YiChin, Peng, WenChih, Lei, PoRuey.: On mining mobile apps usage behavior for predicting apps usage in smartphones. In: Proceedings of the 22nd International Conference on Information and Knowledge Management (CIKM), pp 609–618 (2013)

  36. Lim, S.L., Bentley, P.J., Kanakam, N., Ishikawa, F., Honiden, S.: Investigating country differences in mobile app user behavior and challenges for software engineering. IEEE Trans. Softw. Eng. 41(1), 40–64 (2015)

    Article  Google Scholar 

  37. Lin, J., Sugiyama, K., Kan, M.Y., Chua, T.S.: Addressing cold-start in app recommendation: latent user models constructed from twitter followers. In: Proc. ACM SIGIR, pp 283–292 (2013)

  38. Lin, Z., Althoff, T., Leskovec, J.: I’ll be back: On the multiple lives of users of a mobile activity tracking application. In: Proceedings of the 2018 World Wide Web Conference on World Wide Web, International World Wide Web Conferences Steering Committee, pp 1501–1511 (2018)

  39. Lipsman, A., Lella, A.: The 2017 us mobile app report. 2017. Retrieved from comScore: http://www.comscore.com/Insights/Presentations. Accessed 15 Apr 2019

  40. Malmi, E., Weber, I.: You are what apps you use: Demographic prediction based on user’s apps. In: Tenth International AAAI Conference on Web and Social Media, AAAI (2016)

  41. Meeker, M., Wu, L.: Internet trends 2018. In: Kleiner Perkins (2018)

  42. Moffitt, T.E.: Adolescence-limited and life-course-persistent antisocial behavior: a developmental taxonomy. In: Biosocial Theories of Crime, pp 69–96. Routledge (2017) https://pubmed.ncbi.nlm.nih.gov/8255953/

  43. Ollendick, T.H., Weist, M.D., Borden, M.C., Greene, R.W.: Sociometric status and academic, behavioral, and psychological adjustment: a five-year longitudinal study. J. Consult. Clin. Psychol. 60(1), 80 (1992)

    Article  Google Scholar 

  44. Park, M.H., Hong, J.H., Cho, S.B.: Location-based recommendation system using bayesian user’s preference model in mobile devices. In: Proceedings of the 4th International Conference on Ubiquitous Intelligence and Computing (UIC), pp 1130–1139 (2007)

  45. Peltonen, E., Lagerspetz, E., Hamberg, J., Mehrotra, A., Musolesi, M., Nurmi, P., Tarkoma, S.: The hidden image of mobile apps: Geographic, demographic, and cultural factors in mobile usage. In: Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services, ACM, New York, NY, USA, MobileHCI ’18, pp 10:1–10:12, (2018) https://doi.org/10.1145/3229434.3229474

  46. Pennacchiotti, M., Popescu, A.M.: A machine learning approach to twitter user classification. In: Fifth International AAAI Conference on Weblogs and Social Media (2011)

  47. Preece, J., Shneiderman, B.: The reader-to-leader framework: motivating technology-mediated social participation. AIS Trans. on Hum.-Comput. Interact. 1(1), 13–32 (2009)

    Article  Google Scholar 

  48. Rachuri, K.K., Rachuri, K.K., Rachuri, K.K., Rachuri, K.K., Tapia, E.M., Tapia, E.M.: Mobileminer: mining your frequent patterns on your phone. In: Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), pp 389–400 (2014)

  49. Ros, M., Pegalajar, M., Delgado, M., Vila, A., Anderson, D.T., Keller, J.M., Popescu, M.: Linguistic summarization of long-term trends for understanding change in human behavior. In: 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), IEEE, pp 2080–2087 (2011)

  50. Safavian, S.R., Landgrebe, D.: A survey of decision tree classifier methodology. IEEE Trans. Syst. Man Cybern. 21(3), 660–674 (1991)

    MathSciNet  Article  Google Scholar 

  51. Seneviratne, S., Seneviratne, A., Mohapatra, P., Mahanti, A.: Predicting user traits from a snapshot of apps installed on a smartphone. ACM SIGMOBILE Mob. Comput. Commun. Rev. 18(2), 1–8 (2014)

    Article  Google Scholar 

  52. Seneviratne, S., Seneviratne, A., Mohapatra, P., Mahanti, A.: Your installed apps reveal your gender and more! ACM SIGMOBILE Mob. Comput. Commun. Rev. 18(3), 55–61 (2015)

    Article  Google Scholar 

  53. Shameli, A., Althoff, T., Saberi, A., Leskovec, J.: How gamification affects physical activity: Large-scale analysis of walking challenges in a mobile application. In: Proceedings of the 26th International Conference on World Wide Web Companion, International World Wide Web Conferences Steering Committee, pp 455–463 (2017)

  54. Shi, K., Ali, K.: Getjar mobile application recommendations with very sparse datasets. In: Proc. ACM KDD, pp 204–212 (2012)

  55. Shin, C., Hong, J.H., Dey, A.K.: Understanding and prediction of mobile application usage for smart phones. In: Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), pp 173–182 (2012)

  56. Sigg, S., Peltonen, E., Lagerspetz, E., Nurmi, P., Tarkoma, S.: Exploiting usage to predict instantaneous app popularity: trend filters and retention rates. ACM Trans. WEB 31, 1–25 (2019)

    Article  Google Scholar 

  57. Singh, V.K., Bozkaya, B., Pentland, A.: Money walks: implicit mobility behavior and financial well-being. PLoS One 10(8), e0136628 (2015)

    Article  Google Scholar 

  58. Skinner, B.F.: Science and Human Behavior, vol. 92904. Simon and Schuster, New York (1953)

    Google Scholar 

  59. Sundsøy, P., Bjelland, J., Reme, B.A., Iqbal, A.M., Jahani, E.: Deep learning applied to mobile phone data for individual income classification. In: 2016 International Conference on Artificial Intelligence: Technologies and Applications, Atlantis Press (2016)

  60. Telama, R., Yang, X., Viikari, J., Välimäki, I., Wanne, O., Raitakari, O.: Physical activity from childhood to adulthood: a 21-year tracking study. Am J. Prev. Med. 28(3), 267–273 (2005)

    Article  Google Scholar 

  61. Tu, Z., Li, R., Li, Y., Wang, G., Wu, D., Hui, P., Su, L., Jin, D.: Your apps give you away: distinguishing mobile users by their app usage fingerprints. Proc. ACM Interact. Mob. Wearable Ubiquit. Technol. 2(3), 138 (2018)

    Article  Google Scholar 

  62. Veldhuis, R.: The centroid of the symmetrical kullback-leibler distance. IEEE Signal Process. Lett. 9(3), 96–99 (2002)

    Article  Google Scholar 

  63. Xu, Q., Erman, J., Gerber, A., Mao, Z., Pang, J., Venkataraman, S.: Identifying diverse usage behaviors of smartphone apps. In: Proceedings of the Conference on Internet Measurement Conference (IMC), pp 329–344 (2011)

  64. Xu, F., Xia, T., Cao, H., Li, Y., Sun, F., Meng, F.: Detecting popular temporal modes in population-scale unlabelled trajectory data. Proc. ACM Interact. Mob. Wearable Ubiquit. Technol. 2(1), 46 (2018)

    Article  Google Scholar 

  65. Yang, J., Wei, X., Ackerman, M.S., Adamic, L.A.: Activity lifespan: An analysis of user survival patterns in online knowledge sharing communities. In: Fourth International AAAI Conference on Weblogs and Social Media, AAAI (2010)

  66. Yang, D., Kraut, R., Smith, T., Mayfield, E., Jurafsky, D.: Seekers, providers, welcomers, and storytellers: Modeling social roles in online health communities. In: ACM CHI Conference on Human Factors in Computing Systems, ACM (2019)

  67. Yu, D., Li, Y., Xu, F., Zhang, P., Kostakos, V.: Smartphone app usage prediction using points of interest. Proc. ACM Interact. Mob. Wearable Ubiquit. Technol. 1(4), 174 (2018)

    Article  Google Scholar 

  68. Zastrow, C., Kirst-Ashman, K.: Understanding Human Behavior and the Social Environment. Cengage Learning, Bosotn (2006)

    Google Scholar 

  69. Zhang, M., Tang, J., Zhang, X., Xue, X.: Addressing cold start in recommender systems: a semi-supervised co-training algorithm. In: SIGIR (2014)

  70. Zhao, S., Ramos, J., Tao, J., Jiang, Z., Li, S., Wu, Z., Pan, G., Dey, A.K.: Discovering different kinds of smartphone users through their application usage behaviors. In: Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), pp 498–509 (2016)

  71. Zhu, L., Gonder, J., Lin, L.: Prediction of individual social-demographic role based on travel behavior variability using long-term gps data. J. Adv. Transp. 2017, 1–13 (2017)

  72. Zuniga, A., Flores, H., Lagerspetz, E., Tarkoma, S., Manner, J., Hui, P., Nurmi, P.: Tortoise or hare?: Quantifying the effects of performance on mobile app retention. In: Proceedings of the 2019 World Wide Web Conference (WWW ’19) (2019)

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Yong Li.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Tu, Z., Cao, H., Lagerspetz, E. et al. Demographics of mobile app usage: long-term analysis of mobile app usage. CCF Trans. Pervasive Comp. Interact. (2021). https://doi.org/10.1007/s42486-020-00041-3

Download citation

Keywords

  • App usage
  • Long-term analysis
  • Economic attributes
  • User study