Emergent Community Structure in Social Tagging Systems

TL;DR

This paper introduces a method to detect community structures in social tagging systems by analyzing collective tagging activity, revealing emergent semantic relationships among resources.

Contribution

It presents a novel approach to define resource distance based on user tags, enabling the discovery of emergent community structures in collaborative tagging systems.

Findings

Community structures can be detected using the proposed resource distance.

Detected communities reflect meaningful semantic relations.

Emergent communities arise from uncoordinated user activity.

Abstract

A distributed classification paradigm known as collaborative tagging has been widely adopted in new Web applications designed to manage and share online resources. Users of these applications organize resources (Web pages, digital photographs, academic papers) by associating with them freely chosen text labels, or tags. Here we leverage the social aspects of collaborative tagging and introduce a notion of resource distance based on the collective tagging activity of users. We collect data from a popular system and perform experiments showing that our definition of distance can be used to build a weighted network of resources with a detectable community structure. We show that this community structure clearly exposes the semantic relations among resources. The communities of resources that we observe are a genuinely emergent feature, resulting from the uncoordinated activity of a large number of users, and their detection paves the way for mapping emergent semantics in social tagging systems.