Abstract
With the development of information technologies, Social Media platforms have become popular and accumulated numerous data about individuals’ behavior. It offers a promising opportunity of discovering usable knowledge about the individuals’ movement behavior, which fosters novel applications and services. In this paper, in order to study the relations between communities and location clusters, we propose the index of location entropy to measure the degree of dispersion of the locations in each community, and the index of community entropy to measure the degree of dispersion of the communities in each location cluster. At last, we analyze users’ trajectories and define four Trajectory Patterns. An algorithm is also proposed to extract those patterns from microblog data. We implement the algorithm and find some interesting and useful results for the intelligent recommender systems.
This research was supported by International Science and Technology Cooperation Program of China (Grant No. 2010DFA92720-24), National Natural Science Foundation of China (NSFC) under Grant No. 61303167 and 11271351, and partially supported by Basic Research Program of Shenzhen (Grant No. JCYJ20130401170306838 and JC201105190934A).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhao, Z., Feng, S., Wang, Q., Huang, J.Z., Williams, G.J., Fan, J.: Topic oriented community detection through social objects and link analysis in social networks. Knowl. Based Syst. 26, 164–173 (2012)
Clauset, A., Newman, M.E., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70(6), 066111 (2004)
Cha, M., Mislove, A., Gummadi, K.P.: A measurement-driven analysis of information propagation in the flickr social network. In: Proceedings of the 18th International Conference on World Wide Web, pp. 721–730. ACM (2009)
Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 199–208. ACM (2009)
Liben-Nowell, D., Novak, J., Kumar, R., Raghavan, P., Tomkins, A.: Geographic routing in social networks. Proc. Natl. Acad. Sci. U.S.A. 102(33), 11623–11628 (2005)
Li, C., Zhao, Z., Luo, J., Fan, J.: Info-cluster based regional influence analysis in social networks. In: Advances in Knowledge Discovery and Data Mining, pp. 87–98 (2011)
Humphreys, L.: Mobile social networks and social practice: a case study of dodgeball. J. Comput. Mediated Commun. 13(1), 341–360 (2007)
Yook, S.-H., Jeong, H., Barabási, A.-L.: Modeling the internet’s large-scale topology. Proc. Natl. Acad. Sci. 99(21), 13382–13386 (2002)
Barthelemy, M., Gondran, B., Guichard, E.: Spatial structure of the internet traffic. Physica A: Stat. Mech. Appl. 319, 633–642 (2003)
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the Eleventh International Conference on Data Engineering, pp. 3–14. IEEE (1995)
Han, J., Pei, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.: Prefixspan: mining sequential patterns efficiently by prefix-projected pattern growth. In: ICDE, pp. 215–224, April 2001
Zaki, M.J.: Spade: an efficient algorithm for mining frequent sequences. Mach. Learn. 42(1), 31–60 (2001)
Cao, H., Mamoulis, N., Cheung, D.W.: Mining frequent spatio-temporal sequential patterns. In: Fifth IEEE International Conference on Data Mining, pp. 82–89. IEEE (2005)
Kalnis, P., Mamoulis, N., Bakiras, S.: On discovering moving clusters in spatio-temporal data. In: Advances in Spatial and Temporal Databases, pp. 923–923 (2005)
Mamoulis, N., Cao, H., Kollios, G., Hadjieleftheriou, M., Tao, Y., Cheung, D.W.: Mining, indexing, and querying historical spatiotemporal data. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 236–245. ACM (2004)
Gastner, M.T., Newman, M.E.: The spatial structure of networks. The Eur. Phys. J. B-Condens. Matter Complex Syst. 49(2), 247–252 (2006)
Li, C., Zhao, Z., Liu, S., Yin, L., Luo, J.: Relationships between geographical cluster and cyberspace community: a case study on microblog. In: 2012 20th International Conference on Geoinformatics (GEOINFORMATICS), pp. 1–5. IEEE (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, C., Zhao, Z., Luo, J., Yin, L., Zhou, Q. (2014). A Spatial-Temporal Analysis of Users’ Geographical Patterns in Social Media: A Case Study on Microblogs. In: Han, WS., Lee, M., Muliantara, A., Sanjaya, N., Thalheim, B., Zhou, S. (eds) Database Systems for Advanced Applications. DASFAA 2014. Lecture Notes in Computer Science(), vol 8505. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43984-5_22
Download citation
DOI: https://doi.org/10.1007/978-3-662-43984-5_22
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-43983-8
Online ISBN: 978-3-662-43984-5
eBook Packages: Computer ScienceComputer Science (R0)