ReadBehavior: Reading probabilities modeling of tweets via the users' retweeting behaviors

Along with twitter's tremendous growth, studying users' behaviors, such as retweeting behavior, have become an interesting research issue. In literature, researchers usually assumed that the twitter user could catch up with all the tweets posted by his/her friends. This is untrue most of the time. Intuitively, modeling the reading probability of each tweet is of practical importance in various applications, such as social influence analysis. In this paper, we propose a ReadBehavior model to measure the probability that a user reads a specific tweet. The model is based on the user's retweeting behaviors and the correlation between the tweets' posting time and retweeting time. To illustrate the effectiveness of our proposed model, we develop a PageRank-like algorithm to find influential users. The experimental results show that the algorithm based on ReadBehavior outperforms other related algorithms, which indicates the effectiveness of the proposed model.