Harry Halpin, University of Edinburgh <hhalpin -(a)- ibiblio.org>
In Halpin, Robu, and Shepard, we showed using Kulback-Leibler (KL) divergence that not only do tags form a power law distribution, but that the distribution forms very quickly. Take the KL Divergence between every two consecutive points in time of the distribution, stabilization is when distribution goes to zero.
Originally hypothesized that the "bump" in the data could be caused by a preferential attachment mechanism. However, in an experiment that tested both feedback and no-feedback conditions over tagging (300 subjects) shows that the power-law distribution arises regardless of any feedback effect (Bollen and Halpin, in preparation).
Would an information-theoretic analysis of tags that leads to a power-law without feedback? Perhaps just an idealized information retrieval paradigm, with each group of tags having an entropy assigned to it?
A tag applied to every relevant resource would retrieve every document, and so have an
Applications of similar models to social networks, with aim of surveying, discovering, and finding patterns in social networks?
Harry Halpin, School of Informatics, University of Edinburgh: Interests in Web architecture, analytic and continental philosophy, collective intelligence, natural language processing, and the Semantic Web. W3C (World Wide Web Consortium) Fellowship (MIT/INRIA) starting in January 2009, to launch Social Web W3C Incubator Group