Using Tags and Clustering to Identify Topic-Relevant Blogs
MetadataShow full item record
This item's downloads: 3335 (view details)
Conor Hayes, Paolo Avesani in Nicolas Nicolov, Natalie Glance, Eytan Adar, Mathew Hurst, Mark Lieberman, James H. Martin, Franco Salvetti (editors) "Using Tags and Clustering to Identify Topic-Relevant Blogs", Proceedings of the 1st International Conference on Weblogs and Social Media (ICWSM 07), 2007.
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.