Social tagging systems allow users to easily create, organize and share collections of resources (e.g. Web pages, research papers, photos, etc.) in a collaborative fashion. The rise in popularity of these systems in recent years go along with an rapid increase in the amount of data contained in their underlying folksonomies, thereby hindering the user task of discovering interesting resources. In this paper the problem of filtering resources from social tagging systems according to individual user interests using purely tagging data is studied. One-class classification is evaluated as a means to learn how to identify relevant information based on positive examples exclusively, since it is assumed that users expressed their interest in resources by annotating them while there is not an straightforward method to collect non-interesting information. The results of using social tags for personal classification are compared with those achieved with traditional information sources about the user interests such as the textual content of Web documents. Users finding interesting resources based on social tags is an important benefit of exploiting the collective knowledge generated by these systems. Experimental evaluation showed that tag-based classification outperformed classifiers based on the full-text of documents and other content-related sources.