@unpublished{hayes:ubt,
  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 Semantic Web 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 potentially strong topic 'authorities' in each cluster. We conclude that tags have a key auxiliary role in refining and confirming the information produced using typical knowledge discovery techniques.},
  author = {Hayes, C. and Avesani, P.},
  interhash = {77518d83a3d52d0a104150f9dd7b55ab},
  intrahash = {5d211ac9dd807b795e6f5b87e0488222},
  note = {'tag based clustering may not be adapted for this kind of distribution [power law]'},
  title = {{Using Blog Tags To Identify Topic Authorities}},
  url = {https://apps.lis.uiuc.edu/wiki/display/LEADS/Fall+2007+Archive},
  year = 2007
}

@article{hayes2006aut,
  author = {Hayes, C. and Avesani, P. and Veeramachaneni, S.},
  interhash = {f35063ae2e08a860c0657a6774e68916},
  intrahash = {c381f8e1221ff7451110b9e038670018},
  journal = {ITC-IRST Technical Report. http://sra. itc. it/people/hayes/pubs/06/hayes-ijcai07-tech-report. pdf},
  title = {{An analysis of the use of tags in a blog recommender system}},
  year = 2006
}

@article{cha2007tyt,
  author = {Cha, M. and Kwak, H. and Rodriguez, P. and Ahn, Y.Y. and Moon, S.},
  booktitle = {Proceedings of the 7th ACM SIGCOMM conference on Internet measurement},
  interhash = {d01f4a75a4712049f13c464b31ef6414},
  intrahash = {718bb817f2ee6785831033fe9e4989d0},
  organization = {ACM New York, NY, USA},
  pages = {1--14},
  title = {{I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system}},
  year = 2007
}