Learning user characteristics from social tagging behavior

In social tagging systems the tagging activities of users leave a huge amount of implicit information about them. The users choose tags for the resources they annotate based on their interests, background knowledge, personal opinion and other criteria. Whilst existing research in mining social tagging data mostly focused on gaining a deeper understanding of the user's interests and the emerging structures in those systems, little work has yet been done to use the rich implicit information in tagging activities to unveil to what degree users' tags convey information about their background. The automatic inference of user background information can be used to complete user profiles which in turn supports various recommendation mechanisms. This work illustrates the application of supervised learning mechanisms to analyze a large online corpus of tagged academic literature for extraction of user characteristics from tagging behavior. As a representative example of background characteristics we mine the user's research discipline. Our results show that tags convey rich information that can help designers of those systems to better understand and support their prolific users - users that tag actively - beyond their interests.