WisColl: Collective wisdom based blog clustering

  • Authors:

  • Nitin Agarwal;Magdiel Galan;Huan Liu;Shankar Subramanya

  • Affiliations:

  • Department of Information Science, University of Arkansas at Little Rock, Little Rock, AR 72204, United States;Computer Science and Engineering, Arizona State University, Tempe, AZ 85287, United States;Computer Science and Engineering, Arizona State University, Tempe, AZ 85287, United States;Computer Science and Engineering, Arizona State University, Tempe, AZ 85287, United States

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2010

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Abstract

The Blogosphere is expanding in an unprecedented speed. A better understanding of the blogosphere can greatly facilitate the development of the Social Web to serve the needs of users, service providers, and advertisers. One important task in this process is clustering blog sites. Although a good number of traditional clustering methods exists, they are not designed to take into account the blogosphere unique characteristics. Clustering blog sites presents new challenges. A prominent feature of the Social Web is that many enthusiastic bloggers voluntarily write, tag, and catalog their posts in order to reach the widest possible audience who will share their thoughts and appreciate their ideas. In the process a new kind of collective wisdom is generated. We propose WisColl by tapping into this collective wisdom when clustering blog sites. In this paper, we study how clustering with collective wisdom can be achieved and compare it with a representative traditional clustering method. We present statistical and visual results, report findings and suggest future work extending to many real-world applications.