Micro-blogging activity taking place in sites such as Twitter gains everyday more importance as a source of real-time information and news spreading medium. Finding relevant information sources among the increasing number of Twitter members is essential for users needing to cope with real-time information. In this paper we study Twitter aiming at generating a set of recommendations to a target user consisting in people who publish tweets that might be interesting to him/her. We evaluate and compare two recommendation approaches: the first selects a set of candidate recommendations using only the network topology and the second exploits the user-generated content available in their tweets. We report the results of a set of controlled experiments with real users carried out to evaluate and compare the performance of both algorithms.