Mining users' activity on large Twitter text data

Authors:
Rongze Xia;Yi Han;Yan Jia;Hu Li
Affiliations:
National University of Defense Technology, Changsha, Hunan, China;Peking University, Beijing, P.R. China;National University of Defense Technology, Changsha, Hunan, China;National University of Defense Technology, Changsha, Hunan, China
Venue:
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Year:
2013

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Abstract

Online social network makes people interact with each other frequently. There comes an important question: at what time users always use twitter? How about users' relationship with others? How do the information flow in the network? In this paper, we conducted an experiment on a large twitter dataset, and some interesting user activity patterns have been discovered. We find that people always use twitter at night in a day. People tweet less on weekends than from Monday to Friday. We verify the power-law distribution of the degree in the network. And we propose a text-based user dividing method. We mine users' text data according to this method and divide them into different categories. Finally, we discover the information flow between different categories.