An approach for opinion-demographic-topology based microblog friend recommendation

Abstract

Through the tremendous increase of users on the microblogging social networks with their associated streams of content, the scarcity of one user’s attention arises. The process of filtering such massive content and discovering who other users could be aligned with his own interests would consume much time. Thus, various mechanisms have been investigated to recommend friends by analyzing the posted content, social graph, or user profiles. In this paper, we propose a new approach for microblog friend recommendation based on the opinion, or sentiment, towards the topics in the microblogs combined with the social graph, in addition to the demographic data available in the user profiles, including age, gender, and location. We have deployed a cloud-based recommender service using R language for big data analytics, which applies our proposed approach to gather feedback from real Twitter users. Results show 0.77 average precision value, with 21 % increase rate considering opinion mining.