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).
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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
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DOI: https://doi.org/10.1007/978-3-662-43984-5_22
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