Algorithm for Mining above and Below Average Utility Blogosphere Users in a Blog Network

Authors

  • Khare S HOD Deptt. Of Computer Science & Engineering, Shri Ram Group Of Institutions, Jabalpur (M.P), India
  • Choudhary S HOD Deptt. Of Computer Science & Engineering, Shri Ram Group Of Institutions, Jabalpur (M.P), India

Keywords:

Blog Network, Blogs, Content Power User (CPU), Power User, Document Content Power

Abstract

In the past few years weblogs have become a major channel for publishing content over the Internet. With the popularity of social media as a medium to communication, everyone around the world has started using weblogs as part of their communication strategy. However there remains a void of literature on mining information from blogging, and users still do not have a solid understanding of how and why people are using this tool. This is an exploratory study into the world of blogging, and it aims to add some insight as to what is going on in the blogosphere. As data mining is an important tool for gathering information in any field. Applying this tool in the field of blogosphere is somewhat we are here to discuss about. The thesis aims at gathering information related to the users and documents being published over the internet. We wish to know the documents and the users that are highly active in the blogosphere. This study of our can be conducted by mining high utility documents and users in the blogosphere. This study we have conducted on a new blogging website created by us by using ASP.Net 4.0 as the tool and then applying the code for mining and reporting of the data

References

[1] Seung-Hwan Lim, Sang-Wook Kim, Sunju Park, and Joon Ho Lee“Determining Content Power Users in a Blog Network: An Approach and Its Applications” in September 2011.

[2] N. Agarwal and H. Liu, Modeling and Data Mining in Blogosphere. San Rafael, CA: Morgan and Claypool, 2009.

[3] C. Manning, P. Raghavan, and H. Schutze, Introduction to Information Retrieval. Cambridge, U.K.: Cambridge Univ. Press, 2008.

[4] X. Song, Y. Chi, K. Hino, and B. Tseng, “Mining in social networks information flow modeling based on diffusion rate for prediction and ranking,” in Proc. Int. Conf. WWW, 2007, pp. 191–200.

[5] R. Kumar, J. Novak, and A. Tomkins, “Structure and evolution of online social networks,” in Proc. Int. Conf. Knowl. Discov. Data Mining, ACM SIGKDD, 2006, pp. 611–617.

[6] D. Gruhl, R. Guha, D. Nowell, and A. Tomkins, “Information diffusion through blogspace,” in Proc. Int. Conf. WWW, 2004, pp. 491–501.

[7] D. Kempe, J. Kleinberg, and E. Tardos, “Maximizing the spread of influence through a social network,” in Proc. ACM Int. Conf. Knowl. Discov. Data Mining, SIGKDD, 2003, pp. 137–146.

[8] M. Richardson and P. Domingos, “Mining knowledge-sharing sites for viral marketing,” in Proc. ACM Int. Conf. Knowl. Discov. Data Mining,SIGKDD, 2002, pp. 61–70.

[9] J. Goldenberg, B. Libai, E. Muller, 2001 “Talk of the network pp 211-223.

Downloads

Published

2025-11-25

How to Cite

[1]
S. Khare and S. Choudhary, “Algorithm for Mining above and Below Average Utility Blogosphere Users in a Blog Network”, Int. J. Comp. Sci. Eng., vol. 7, no. 10, pp. 29–34, Nov. 2025.