Information Diffusion Analysis of Rumor Dynamics over a Social-interaction based Model

Rumors may potentially cause undesirable effect such as the widespread panic in the general public. Especially, with the unprecedented growth of different types of social and enterprise networks, rumors could reach a larger audience than before. Many researchers have proposed different approaches to analyze and detect rumors in social networks. However, most of them either study on theoretical models without real data experiments or use content-based analysis and limited information diffusion analysis without fully considering social interactions. In this paper, we propose a social interaction based model FAST by taking four major properties of social interactions into account including familiarity, activeness, similarity, and trustworthiness. Also, we evaluate our model on real data from Sina Weibo (Twitter-like social network in China), which contains around 200 million tweets and 14 million Weibo users. Based on our model, we create a new metrics Fractional Directed Power Community Index (FD-PCI) derived from mu-PCI to identify the influential spreaders in social networks. FD-PCI shows better performance than conventional metrics such as K-core index and PageRank. Moreover, we obtain interesting influential features to detect rumors by the comparison between rumor and real news dynamics.