A Study of Human Flesh Search Based on SIR Flooding on Scale-Free Networks

Abstract

With the development of social networks, information is shared, amended, and integrated among users. Meanwhile, some questions begin to generate public interests. How does the information propagate in the network? How do human factors affect the spreading patterns of information? How do we construct models to understand the collective group behavior based on probabilistic individual choices? We try to answer the above questions by investigating “Human Flesh Search” (HFS), a phenomenon of digging out full privacy information of a target person with the help of massive collaboration of netizens by integrating information pieces during propagation. SIR model, which is often used to study epidemic diseases, is employed to provide a mathematical explanation of the process of HFS. Experimental results reveal that information entropy has significant influence on the network topology, which in turn affects the probability of affecting network neighbors and finally results in different efficiency of information spreading.