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

Jianguang Du, Dandan Song, Lejian Liao, Xin Li, Li Liu, Guoqiang Li, Guanguo Gao, Guiying Wu

科研成果: 期刊稿件会议文章同行评审

1 引用 (Scopus)

摘要

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.

指纹

探究 'ReadBehavior: Reading probabilities modeling of tweets via the users' retweeting behaviors' 的科研主题。它们共同构成独一无二的指纹。

引用此

Du, J., Song, D., Liao, L., Li, X., Liu, L., Li, G., Gao, G., & Wu, G. (2014). ReadBehavior: Reading probabilities modeling of tweets via the users' retweeting behaviors. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8443 LNAI(PART 1), 114-125. https://doi.org/10.1007/978-3-319-06608-0_10