The complex dynamics of collaborative tagging

Abstract

The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including whether coherent categorization schemes can emerge from unsupervised tagging by users. This paper uses data from tagged sites on the social bookmarking site del.icio.us to examine the dynamics of collaborative tagging systems. In particular, we examine whether the distribution of the frequency of use of tags for "popular" sites with a long history (many tags and many users) can be described by a power law distribution, often characteristic of what are considered complex systems. We produce a generative model of collaborative tagging in order to understand the basic dynamics behind tagging, including how a power law distribution of tags could arise. We empirically examine the tagging history of sites in order to determine how this distribution arises over time and patterns prior to a stable distribution. Lastly, by focusing on the high-frequency tags of a site where the distribution of tags is a stabilized power law, we show how tag co-occurrence networks for a sample domain of tags can be used analyze the meaning of particular tags given their relationship to other tags.

TagMe!: Enhancing Social Tagging with Spatial Context

“…Formal folksonomy models are e.g. presented in [7,8]. They are based on bindings between users, tags, and resources.…”

The Impact of Multifaceted Tagging on Learning Tag Relations and Search

“…Different studies showed already that it is possible to learn hierarchical relations among tags by exploiting traditional folksonomy structures. For example, Halpin et al extract sub-class relations by exploiting correlations between tags induced by co-occurrence of tags [14] and Mika mines narrower and broader concepts by detecting communities and sub-communities within the tripartite graph spanned by traditional folksonomies [15] (cf. weighted tripartite graph in Section 3.1).…”

Neighborhood-Based Tag Prediction

“…The work by Mika [17] considers a tri-partite model of instances, users, and concepts and mines the relationships among these three entities. [8,16,7] have concentrated on understanding the tagging process and the resulting social annotations by examining tag distributions, constructing tag-tag correlation networks and extracting statistics of users' tagging behavior. [14] investigate the overlap between anchortext and tags for a Web page.…”