Information Propagation with Retweet Probability on Online Social Network

• 作者: Kai Deng Qiguang Miao Ruihong Hou Xing Tang Yining Quan
• 来源期刊: 国际计算机前沿大会会议论文集
• 学科: 社会科学
• 关键词: retweet PROBABILITY online social network INFECTIOUS MODEL DIFFUSION MODEL LOGISTIC regression
• 年，卷（期）: 2015,（1）
• 页码范围: 95-96
• 页数: 2页
• 分类号: C5

摘要

The rapid development of online social network has attracted a lot of research attention. On online social network, people can discuss their ideas, express their interests and opinions, all of which are demonstrated by information propagation. So how to model the information propagation cascade accurately has become a hot topic. In this paper, we firstly incorporate the retweet probability into the traditional propagation models. To find the accurate retweet probability, we introduce the logistic regression model for every user based on the extracted features. With the crawled real dataset, simulation is conducted on the real online social network and moreover some novel results have been obtained. The homogenous retweet probability in the original model has underestimated the speed of information propagation, despite the scale of information propagation is almost at the same level. Besides, the initial information poster is really important for a certain propagation, which enables us to make effective strategies to prevent epidemics of rumor on social network.