Extracting Usage Patterns and the Analysis of Tag Connection Dynamics within Collaborative Tagging Systems

Collaborative tagging has become a very popular way of annotation, thanks to the fact that any entity may be labeled by any individual based on his own reason. In this paper we present the results of the case study carried out on the basis of data gathered at different time intervals from the social tagging system developed and implemented on ntelepciune.ro. Analyzing collective data referring to the way in which community members associate different tags, we have observed that between tags, links are formed which become increasingly stable with the passing of time. Following the application of methodology specific to network analysis, we have managed to extract information referring to tag popularity, their influence within the network and the degree to which a tag depends upon another. As such, we have succeeded in determining different semantic structures within the collective tagging system and see their evolution at different stages in time. Furthermore, we have pictured the way in which tag rec-ommendations can be executed and that they can be integrated within recommendation sys-tems. Thus, we will be able to identify experts and trustworthy content based on different cat-egories of interest.