Recently,the rapid growth of social networks has provided rich contents and yet a huge challenge for recommender systems.To better uncover its underlying role in information filtering,a hybrid algorithm was proposed based on the integrated effect of social media and social networks.The social interests(so-called tags)from social media were extracted by natural language process technology;the social influence was measured based on social network analysis;and a tunable parameter to integrate those two effects was adopted to provide recommendation results.Numerical results on a real-world dataset,Newzan,show that the presented model outperforms the classical user-based collaborative filtering algorithm in various metrics,including AUC(area under the curve),precision,recall,diversity and novelty.Furthermore,the case studies on three typical users from Newzan were performed.Statistical analyses show that the optimal recommendation parameter should vary from user to user according to the degree of their social involvement.The results can provide an in-depth understanding for unifying social media to develop social applications.