Using Blog Content Depth and Breadth to Access and Classify Blogs

Blogs are attractive to readers and researchers because of their ability to express a variety of opinions and critiques on topics. In particular, the fast growth of such online context brings in a strong demand for developing blog-specific filtering systems in order to identify and monitor the knowledge flow in the blogosphere. Traditional measures, which simply adopt link-analysis algorithms, are inadequate when it comes to blogs because of their sparseness of links. In this paper, we propose a filtering system that is able to assess the associated content of blog entries by measuring topic concentration and topic variety in terms of content depth and breadth with the scalars of informativeness, completeness, topic count, inter-topic distance, and topic mergence. These employed measures have proved to be appropriate for helping users to judge blogs they prefer among search results. Our system is different from existing blog search engines, as it aims to provide better relevance and precision of the search.