CONICET | Buscador de Institutos y Recursos Humanos

Nowadays, more and more users keep up with news through information streams coming from real-time micro-blogging activity oered by services such as Twitter. In these sites, information is shared via a followers/followees social network structure in which a follower receives all the micro-blogs from his/her followees. Recent research eorts on understanding micro-blogging as a novel form of communication and news spreading medium, have identied three different categories of users in these systems: information sources, information seekers and friends. As social networks grow in the number of registered users, finding relevant and reliable users to receive interesting information becomes essential. In this paper we propose a followee recommender system based on both the analysis of the content of micro-blogs to detect users' interests and in the exploration of the topology of the network to nd candidate users for recom-mendation. Experimental evaluation was conducted in order to determine the impact of dierent proling strategies based on the text analysis of micro-blogs as well as several factors that allows the identication of users acting as good information sources. We found that user-generated content available in the network is a rich source of information for proling users and nding like-minded people.