Tracking User Attention in Collaborative Tagging Communities

Collaborative tagging has recently attracted the attention of both industry and academia due to the popularity of content-sharing systems such as CiteULike, del.icio.us, and Flickr. These systems give users the opportunity to add data items and to attach their own metadata (or tags) to stored data. The result is an effective content management tool for individual users. Recent studies, however, suggest that, as tagging communities grow, the added content and the metadata become harder to manage due to an ease in content diversity. Thus, mechanisms that cope with increase of diversity are fundamental to improve the scalability and usability of collaborative tagging systems. This paper analyzes whether usage patterns can be harnessed to improve navigability in a growing knowledge space. To this end, it presents a characterization of two collaborative tagging communities that target scientific literature: CiteULike and Bibsonomy. We explore three main directions: First, we analyze the tagging activity distribution across the user population. Second, we define new metrics for similarity in user interest and use these metrics to uncover the structure of the tagging communities we study. The structure we uncover suggests a clear segmentation of interests into a large number of individuals with unique preferences and a core set of users with interspersed interests. Finally, we offer preliminary results that demonstrate that the interest-based structure of the tagging community can be used to facilitate content usage as communities scale.