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Emergence of consensus and shared vocabularies in collaborative tagging systems

Record type: Article

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. Second, we study the information structures that emerge from collaborative tagging, namely tag correlation (or folksonomy) graphs. We show how community-based network techniques can be used to extract simple tag vocabularies from the tag correlation graphs by partitioning them into subsets of related tags. Furthermore, we also show, for a specialized domain, that shared vocabularies produced by collaborative tagging are richer than the vocabularies which can be extracted from large-scale query logs provided by a major search engine. Although the empirical analysis presented in this article is based on a set of tagging data obtained from del.icio.us, the methods developed are general, and the conclusions should be applicable across other websites that employ tagging.

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Citation

Robu, Valentin, Halpin, Harry and Shepherd, Hana (2009) Emergence of consensus and shared vocabularies in collaborative tagging systems ACM Transactions on the Web, 3, (4), pp. 1-34.

More information

Published date: September 2009
Keywords: Collaborative tagging, community identification algorithms, complex systems, del.icio.us, emergent semantics, graphical models, knowledge extraction, power laws, search engines
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 268192
URI: http://eprints.soton.ac.uk/id/eprint/268192
PURE UUID: fe3269c9-f30f-4208-98af-38e114d6407d

Catalogue record

Date deposited: 11 Nov 2009 17:32
Last modified: 24 Jul 2017 16:40

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Contributors

Author: Valentin Robu
Author: Harry Halpin
Author: Hana Shepherd

University divisions


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