Tweets Reading Probability Analysis Based on Competing-Window Model

In microgblogs, a user usually follows or is followed by many other users. The content updating and reading is a complex process involving intensive interactions among publishers and readers. It also forms the basis of information diffusion in social networks. In the situation of massive followers, tweets reading would heavily depend on user behaviors and interactions. The tweets reading probability (TRP) would be a vital parameter measuring the effectiveness and influence of tweets. Our work proposed a fundamental model, namely competing-window, to simulate the process of multi-node interactions and analyzed TRP in social network. Based on Sina Microblog, we built a standard data set and run massive experiments on empirical data to extract user behavior patterns. By adopting simulating approaches, TRP in a none-preference social network was obtained. The results indicate that typical TRP is about 8% and different user behaviors affect TRP differently.