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 will receive 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 dierent categories of users in these systems: information sources, information seekers and friends. As the social network grows in the number of registered users, nding 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 nd candidate users for recommendation. 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.
For user recommendation, Armentano et.al REF examined the topology of followers/followees network and identified the relevant users using social relation factors.
Followee recommendation based on text analysis of micro-blogging activity
Inf. Syst.
Inf. Syst.
Computer Science