The complex dynamics of collaborative tagging

TLDR

The Web community debates whether formal classifications or collaborative tagging best organize information, and it remains unclear if coherent categories can emerge from unsupervised user tagging. The study aims to investigate the dynamics of collaborative tagging, specifically whether tag usage follows a power‑law distribution and to develop a generative model explaining this behavior. Using data from the social bookmarking site delicio, the authors analyze tagging histories over time to identify how power‑law distributions arise and to characterize patterns before stabilization. The authors demonstrate that, for sites with a stabilized power‑law tag distribution, tag co‑occurrence networks can reveal the semantic relationships among high‑frequency tags.

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 the social bookmarking site delicio. 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 to determine the 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 to analyze the meaning of particular tags given their relationship to other tags.