Abstract
This paper investigated the relationship between incrementally logged phone logs and self-reported survey data to derive regularity and predictability from mobile phone usage logs. First, we extracted information not from a single value such as location or call logs, but from multivariate contextual logs. Then we considered the changing pattern of the incrementally logged information over time. To evaluate the patterns of human behavior, we applied entropy changes and the duplicated instances ratios from the stream of mobile phone usage logs. By applying the Hidden Markov Models to the patterns, the accumulated log patterns were classified according to the self-reported survey data. This research confirmed that regularity and predictability of human behavior can be evaluated by mobile phone usages.
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References
Song, C., Qu, Z., Blumm, N., Barabasi, A.L.: Limits of predictability in human mobility. Science 327(5968), 1018–1021 (2010)
Barabasi, A.L.: Bursts, the hidden pattern behind everything we do. Dutton, New York (2010)
Massachusetts Institute of Technology, Reality Mining Project, http://reality.media.mit.edu/
Kwok, R.: Personal technology: Phoning in data. Nature 458(7241), 959–961 (2009)
Song, C., Qu, Z., Blumm, N., Barabasi, A.L.: Limits of predictability in human mobility - supplementary material (2010)
Eagle, N., Pentland, A., Lazer, D.: Inferring social network structure using mobile phone data. In: Proceedings of the National Academy of Sciences, pp. 15274–15278 (2009)
Eagle, N., Pentland, A.: Reality mining: sensing complex social systems. Personal and Ubiquitous Computing 10(4), 255–268 (2006)
Cao, L.: In-depth Behavior Understanding and Use: the Behavior Informatics Approach. Information Science 180(17), 3067–3085 (2010)
Phithakkitnukoon, S., Dantu, R.: Adequacy of data for characterizing caller behavior. In: Proceedings of KDD Inter. Workshop on Social Network Mining and Analysis (2008)
Phithakkitnukoon, S., Horanont, T., Di Lorenzo, G., Shibasaki, R., Ratti, C.: Activity-Aware Map: Identifying Human Daily Activity Pattern Using Mobile Phone Data. In: Salah, A.A., Gevers, T., Sebe, N., Vinciarelli, A. (eds.) HBU 2010. LNCS, vol. 6219, pp. 14–25. Springer, Heidelberg (2010)
Jensen, B.S., Larsen, J., Hansen, L.K., Larsen, J.E., Jensen, K.: Predictability of Mobile Phone Associations. In: Inter. Workshop on Mining Ubiquitous and Social Environments (2010)
Chi, J., Jo, H., Ryu, J.H.: Predicting Interpersonal Relationship based on Mobile Communication Patterns. In: The ACM Conf. on Computer Supported Cooperative Work (2010)
Kim, H., Kim, I.J., Kim, H.G., Park, J.H.: Adaptive Modeling of a User’s Daily Life with a Wearable Sensor Network. In: Tenth IEEE International Symposium on Multimedia, pp. 527–532 (2008)
MacKay, D.J.C.: Information theory, inference, and learning algorithms. Cambridge Univ. Press, Cambridge (2003)
Dietterich, T.G.: Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation 10(7), 1895–1923 (1998)
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Kim, H., Park, JH. (2012). Evaluating the Regularity of Human Behavior from Mobile Phone Usage Logs. In: Cao, L., Huang, J.Z., Bailey, J., Koh, Y.S., Luo, J. (eds) New Frontiers in Applied Data Mining. PAKDD 2011. Lecture Notes in Computer Science(), vol 7104. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28320-8_1
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DOI: https://doi.org/10.1007/978-3-642-28320-8_1
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