Measuring Pair-Wise Social Influence in Microblog

The development of Microblog services has created an unprecedented opportunity for people to share information. To better understand the information propagation behaviors in such social networks, an important task is to measure the influence among users. A number of previous works measure users' influence through analyzing the network characteristics or by retweet rate. However, high in degree not necessarily means influential and retweet rate fluctuates over time. In this paper, we propose a user interaction model in microblog by considering the following three key factors: user's active level, user's willingness to retweet, and the influence between a pair of users. One advantage of this model is that the model fitting only requires a sub graph and hence may be performed in a piece-wise fashion. Furthermore, we can find the users with potential influence in the network. We fit the model with a Sina Microblog dataset. We show that this model is able to predict influence at high accuracy. Moreover, this model can be used to predicting retweet rate and finding influential users.