Measuring User Influence in Sina Microblogging Social Network

Article Preview

Abstract:

As a convenient and popular social network platform, microblogging has become an important medium for people’s information exchange. The development of microblogging network can be regarded as a growth of complex network, and dissemination of information in microblogging network shows strong regularity. In the face of big scale of users, the appraisal of user influence become an important index to measures the value of a user. How to recognize user influence becomes a hot topic in microblogging research. In this paper, we proposed a method named InformationRank to evaluate user influence, which based on measure the information of node betweenness centrality in the network. Then, we analyze and compare the efficiency of this method with PageRank algorithm and Hits algorithm. The experimental results indicate that InformationRank algorithm is effective in a large scale network.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

1233-1238

Citation:

Online since:

September 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Kwak H, Lee C, Park H, Moon S. What is Twitter, a social network or a news media? Proceedings of the 19th International Conference on World Wide Web, (2010), pp.591-600.

DOI: 10.1145/1772690.1772751

Google Scholar

[2] Youngsang Cho, Junseok Hwang, Daeho Lee, Identification of effective opinion leaders in the diffusion of technological innovation: A social network approach, Technological Forecasting & Social Change, Vol. 79 (2012), pp.97-106.

DOI: 10.1016/j.techfore.2011.06.003

Google Scholar

[3] Cha M, Haddadi H, Benevenuto F, et al. Measuring User Influence in Twitter: The Million Follower Fallacy. ICWSM, Vol. 10 (2010), pp.10-17.

DOI: 10.1609/icwsm.v4i1.14033

Google Scholar

[4] Shaozhi Ye, Felix Wu. Measuring message propagation and social influence on Twitter. com. lecture Notes in Computer Science, LNCS 6430 (2010), pp.216-231.

Google Scholar

[5] Hao Guo, Yuliang Lu, et al. Measuring user influence of a microblog based on information diffusion. Journal of Shangdong University(Nature Science). Vol. 47 (2012), p.5.

Google Scholar

[6] Meeyoung Cha, Measuring user influence in Twitter: The million follower fallacy Proceedings of International Conference on Weblogs and Social Media, (2010), pp.221-224.

Google Scholar

[7] Jianshu Weng, Eepeng Lim, et al. TwitterRank: Finding Topic-sensitive influential Twitterers. Proceeding of the Third ACM International Conference on Web Search and Data Mining, (2010), pp.261-270.

DOI: 10.1145/1718487.1718520

Google Scholar

[8] Shaoqin Chen, Lei, Li, Jianhua Fan. MURank: An algorithm for social network real-time user's influence. Information Security and Communications Privacy, Vol. 3 (2013), pp.50-52.

Google Scholar