Emergence of consensus and shared vocabularies in collaborative tagging systems
ACM Trans. Web, 3 (4):
1--34 (2009)DOI: http://doi.acm.org/10.1145/1594173.1594176
Abstract
This article uses data from the social bookmarking site del.icio.us to empirically examine the dynamics of collaborative tagging systems and to study how coherent categorization schemes emerge from unsupervised tagging by individual users. First, we study the formation of stable distributions in tagging systems, seen as an implicit form of “consensus” reached by the users of the system around the tags that best describe a resource. We show that final tag frequencies for most resources converge to power law distributions and we propose an empirical method to examine the dynamics of the convergence process, based on the Kullback-Leibler divergence measure. The convergence analysis is performed for both the most utilized tags at the top of tag distributions and the so-called long tail.
Description
Emergence of consensus and shared vocabularies in collaborative tagging systems
