Nowadays, more and more users keep up with news through information streams coming from real-time micro-blogging activity offered by services such as Twitter. In these sites, information is shared via a followers/followees social network structure in which a follower will receive all the micro-blogs from his/her followees. Recent research efforts on understanding micro-blogging as a novel form of communication and news spreading medium, have identified three different categories of users in these systems: information sources, information seekers and friends. As the social network grows 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 the analysis of the content of micro-blogs to detect users' interests and in an exploration of the topology of follower/followee network to find candidate users for recommendation. Experimental evaluation was conducted in order to determine the impact of different profiling strategies based on the text analysis of micro-blogs as well as different factors that allows the identification of users acting as good information sources. We found that user-generated content available in the network is a rich source of information for profiling users and finding like-minded people.
Researchers: Marcelo G. Armentano, Daniela Godoy
Students: Ing. Mauricio Payetta, Ing. Rodrigo Molinas y Vilas, Ing. Joan Sol Roo, Andrea Toyos Pittelli, Ana Laura Lamorte