An influence strength measurement via time-aware probabilistic generative model for microblogs

Abstract

Micro-blogging such as Twitter provides users a platform to participate in the discussion of topics and find more interesting friends. While this large number can generate notable diversity and not all influence strengths between users are the same, it also makes measure the influence strength more accurately, that not only rated as binary friendship relations, challenging and interesting. In this work, we develop a time-aware probabilistic generative model to estimate the influence strength by taking the time interval, relationship of following, and the post content into consideration. In particular, the Gibbs sampling is employed to perform approximate inference, and the interval of time and the multi-path influence propagation is incorporated to estimate the indirect influence strength more microscopically according to the propagation of words. Comprehensive experiments has been conducted on a real data set from Twitter, which contains about 0.26 million users and 2.7 million tweets, to evaluate the performance of our proposed approach. As indicated, the experimental results validate the effectiveness of our approach. Furthermore, we also observe that the influence strength ranking by our model is less correlative with the method which ranks the influence strength according to the number of common friends. © 2013 Springer-Verlag.