Propagation Model of Derivative Rumor Considering Propagation Error and Malicious Tampering

In the era of big data, the speed and scope of information diffusion have been greatly improved. However, rumor would inevitably be derived from the process of information diffusion and have important impact on information security and social stability. The current research about rumor propagation mainly focuses on pinching rumor and lacks attention to the derivative rumor. In this paper, we construct an improved linear threshold model and two indicators of network information entropy and opinion to analyze the propagation regular of derivative rumor. Based on a real social network dataset, this paper studied the effects of propagation errors, malicious node proportion, and tampering probability for the derivative rumor propagation. Experimental results showed that the increasing of propagation error can significantly increase the confusion of system, and make the distribution of opinion from exponential to normal. Increasing the proportion of malicious nodes obvious improve the number of individuals spread false information. The effect of tampering possibility is closely related to the proportion of malicious nodes. The moderate probability of tampering with high proportion of malicious nodes cause system confusion to peak.