Topical tags vs non-topical tags: Towards a bipartite classification?

In this paper we investigate whether it is possible to create a computational approach that allows us to distinguish topical tags (i.e. talking about the topic of a resource) and non-topical tags (i.e. describing aspects of a resource that are not related to its topic) in folksonomies, in a way that correlates with humans. Towards this goal, we collected 21 million tags (1.2 million unique terms) from Delicious and developed an unsupervised statistical algorithm that classifies such tags by applying a word space model adapted to the folksonomy space. Our algorithm analyses the co-occurrence network of tags to a target tag and exploits graph-based metrics for their classification. We validated its outcomes against a reference classification made by humans on a limited number of terms in three separate tests. The analysis of the outcomes of our algorithm shows, in some cases, a consistent disagreement among humans and between humans and our algorithm about what constitutes a topical tag, and suggests the rise of a new category of overly generic tags (i.e. umbrella tags).