Using Tags and Clustering to Identify Topic-Relevant Blogs

Abstract

The Web has experienced an exponential growth in the use of weblogs or blogs. Blog entries are generally organised using tags, informally defined labels which are increasingly being proposed as a `grassroots¿ answer to SemanticWeb standards. Despite this, tags have been shown to be weak at partitioning blog data. In this paper, we demonstrate how tags provide useful, discriminating information where the blog corpus is initially partitioned using a conventional clustering technique. Using extensive empirical evaluation we demonstrate how tag cloud information within each cluster allows us to identify the most topic-relevant blogs in the cluster. We conclude that tags have a key auxiliary role in refining and confirming the information produced using typical knowledge discovery techniques.