ExaMine: Dynamic Latent Expertise Mining in Social Networks

Nir Ofek, Asaf Shabtai

With an ever increasing number of individuals using social networks and the wide range of activities these platforms provide, there is a growing need to develop knowledge extraction methods. In this study we present a system for identifying expertise found within a user’s social network connections (ie, friends). During the learning phase, the system generates a profile for each connection by mining the activities associated with each connection. Then, when the user browses the Web, the system actively retrieves an ordered list of connections for any Web page that is viewed; these connections are identified as experts on the dynamic topic (s) of the Web page according to a classification process. Our evaluation shows promising results for retrieved connections with their true areas of expertise, where the mean average precision over all experimented topics is 0.60, outperforming a human baseline.