  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.
