Information vs Interaction: An Alternative User Ranking Model for Social Networks

The recent years have seen an unprecedented boom of social network services, such as Twitter, which boasts over 200 million users. In such big social platforms, the influential users are ideal targets for viral marketing to potentially reach an audience of maximal size. Most proposed algorithms rely on the linkage structure of the respective underlying network to determine the information flow and hence indicate a users influence. From social interaction perspective, we built a model based on the dynamic user interactions constantly taking place on top of these linkage structures. In particular, in the Twitter setting we supposed a principle of balanced retweet reciprocity, and then formulated it to disclose the values of Twitter users. Our experiments on real Twitter data demonstrated that our proposed model presents different yet equally insightful ranking results. Besides, the conducted prediction test showed the correctness of our model.