JCC  Vol.2 No.1 , January 2014
Application of Association Rule Mining Theory in Sina Weibo
Author(s) Xiao Cui, Hao Shi, Xun Yi
Abstract

A user profile contains information about a user. A substantial effort has been made so as to understand users’ behavior through analyzing their profile data. Online social networks provide an enormous amount of such information for researchers. Sina Weibo, a Twitter-like microblogging platform, has achieved a great success in China although studies on it are still in an initial state. This paper aims to explore the relationships among different profile attributes in Sina Weibo. We use the techniques of association rule mining to identify the dependency among the attributes and we found that if a user’s posts are welcomed, he or she is more likely to have a large number of followers. Our results demonstrate how the relationships among the profile attributes are affected by a user’s verified type. We also put some efforts on data transformation and analyze the influence of the statistical properties of the data distribution on data discretization.


Cite this paper
Cui, X. , Shi, H. and Yi, X. (2014) Application of Association Rule Mining Theory in Sina Weibo. Journal of Computer and Communications, 2, 19-26. doi: 10.4236/jcc.2014.21004.
References

[1]   C. A. Lampe, N. Ellison and C. Steinfield, “A Familiar Face (Book): Profile Elements as Signals in an Online Social Network,” Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2007, pp. 435-444.

[2]   A. Mislove, B. Viswanath, K. P. Gummadi and P. Druschel, “You Are Who You Know: Inferring User Profiles in Online Social Networks,” Proceedings of the Third ACM International Conference on Web Search and Data Mining, 2010, pp. 251-260.

[3]   D. Quercia, M. Kosinski, D. Stillwell and J. Crowcroft, “Our Twitter Profiles, Our Selves: Predicting Personality with Twitter,” 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust (Passat) and 2011 IEEE Third International Conference on Social Computing (Socialcom), 2011, pp. 180-185.

[4]   D. Clark, R. Crandall and Y. Mei, “4th Annual China 2.0 Conference Underscores Business Innovation, Social Impact and U.S.-China Links,” 2013. http://sprie.gsb.stanford.edu/news/4th_annual_china _20_confe-rence_underscores_business_innovation_social_impact_and_uschina_links_20131022/

[5]   Z. Guo, Z. Li, H. Tu and L. Li, “Characterizing User Behavior in Weibo,” 2012 Third FTRA International Conference on Mobile, Ubiquitous, and Intelligent Computing (MUSIC), 2012, pp. 60-65.

[6]   J. Chen and J She, “An Analysis of Verifications in Microblogging Social Networks—Sina Weibo,” 2012 32nd International Conference on Distributed Computing Systems Workshops (ICDCSW), 2012, pp. 147-154.

[7]   C. Wang, X. Guan, T. Qin and W. Li, “Who Are Active? An In-Depth Measurement on User Activity Characteristics in Sina Microblogging,” Global Communications Conference (GLOBECOM), 2012, pp. 2083-2088.

[8]   Sina Open API. http://open.weibo.com/wiki/

[9]   J. Han, M. Kamber and J. Pei, “Data Mining: Concepts and Techniques,” Morgan Kaufmann, 2006.

[10]   J. M. Juran and A. B. Godfrey, “Juran’s Quality Handbook (Vol.2),” McGraw Hill, New York, 1999.

[11]   I. Frohne and R. J. Hyndman, “Sample Quantiles,” R Project, 2009.

[12]   J. Feng, “Romancing the Internet: Producing and Consuming Chinese Web Romance,” Brill, 2013. http://dx.doi.org/10.1163/9789004259720

 
 
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