Evaluation of Microblog Users’ Influence Based on PageRank and Users Behavior Analysis
ABSTRACT
This paper explores the uses’ influences on microblog. At first, according to the social network theory, we present an analysis of information transmitting network structure based on the relationship of following and followed phenomenon of microblog users. Informed by the microblog user behavior analysis, the paper also addresses a model for calculating weights of users’ influence. It proposes a U-R model, using which we can evaluate users’ influence based on PageRank algorithms and analyzes user behaviors. In the U-R model, the effect of user behaviors is explored and PageRank is applied to evaluate the importance and the influence of every user in a microblog network by repeatedly iterating their own U-R value. The users’ influences in a microblog network can be ranked by the U-R value. Finally, the validity of U-R model is proved with a real-life numerical example.
This paper explores the uses’ influences on microblog. At first, according to the social network theory, we present an analysis of information transmitting network structure based on the relationship of following and followed phenomenon of microblog users. Informed by the microblog user behavior analysis, the paper also addresses a model for calculating weights of users’ influence. It proposes a U-R model, using which we can evaluate users’ influence based on PageRank algorithms and analyzes user behaviors. In the U-R model, the effect of user behaviors is explored and PageRank is applied to evaluate the importance and the influence of every user in a microblog network by repeatedly iterating their own U-R value. The users’ influences in a microblog network can be ranked by the U-R value. Finally, the validity of U-R model is proved with a real-life numerical example.
Cite this paper
L. Huang and Y. Xiong, "Evaluation of Microblog Users’ Influence Based on PageRank and Users Behavior Analysis," Advances in Internet of Things, Vol. 3 No. 2, 2013, pp. 34-40. doi: 10.4236/ait.2013.32005.
L. Huang and Y. Xiong, "Evaluation of Microblog Users’ Influence Based on PageRank and Users Behavior Analysis," Advances in Internet of Things, Vol. 3 No. 2, 2013, pp. 34-40. doi: 10.4236/ait.2013.32005.
References
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[1] Baidu Encyclopedia, “MicroBlog,” 2012. http://baike.baidu.com/view/1567099.html [2] A. Java, X. Song, T. Finin and B. Tseng, “Why We Twitter: Understanding Microblogging Usage and Communities,” Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, San Jose, 12-15 August 2007, pp. 56-65. [3] A. R. M. Teutle, “Twitter: Network Properties Analysis,” 20th International Conference on Electronics Communications and Computer, Cholula, 2010, pp. 80-186. [4] B. Krishnamurthy, P. Gill and M. Arlitt, “A Few Chirps about Twitter,” Proceedings of the 1st Workshop on Online Social Networks, Seattle, 2008, pp.19-24. [5] S. Cai and J. Zhang, “Method of Micro-Blogging Hotspot Discovery Based on Centralization,” Chinese Journal of Management, Vol. 9, No. 6, 2012, pp. 874-879. [6] G. Weimann, “The Influentials: People Who Influence People,” State University of New York Press, Albany, 1994, pp. 468-470. [7] Y. Yamaguchi, “TURank: Twitter User Ranking Based on User-Tweet Graph Analysis,” Web Information Systems Engineering, Vol. 6488, 2010, pp. 240-253. [8] Klout, 2012. http://klout.com [9] S. Kang, “Evaluation of the Social Network’s Nodes Influence Based on Users’ Behavior and Relationship— Take Microblogging for Instance,” Master Thesis, Beijing University of Posts and Telecommunications, Beijing, 2011. [10] J. Cao, “Technique Analysis of Page Rank in Google,” Journal of Information, Vol. 19, No. 10, 2002, pp. 15-18. [11] W. Ma, “Study on Web Information Credibility Evaluation Method Based on Improved Page Rank,” Master Thesis, Hebei University, Baoding, 2011. [12] “Tencent Microblog Data Source,” 2012. http://www.Kddcup2012.Org/c/kddcup 2012-track