An Efficient Rumor Suppression Approach With Knowledge Graph Convolutional Network in Social Network
Fei Gao, Qiang He, Xingwei Wang, Lin Qiu, Min Huang
Abstract
Social networks currently serve as one of the primary sources from which people obtain news, with the spread of rumors emerging as a major concern. The goal of rumor suppression is to minimize the number of individuals affected by rumors through various methods, such as blocking and disseminating the truth. Although this problem has evolved into a popular research topic, existing solutions often overlook the temporal impact of rumor-refuting information and the influence of user opinions on rumor spreading. In the study, we first investigate the two-stage rumor minimization problem. The problem primarily considers two situations about only the propagation of rumors and the simultaneous propagation of rumor and rumor-refuting information, aiming to minimize the impact of rumors. We propose the two-stage user opinion rumor propagation model (TSUORP), which fully incorporates the timing of official releases of rumor-refuting information and their influence on the generation of rumors propagation. Based on this, we propose an approach using the knowledge graph convolutional network (KGCN) algorithm to rapidly and effectively select rumor-refuting information seed nodes based on user opinions. To assess the validity of our proposed approach, we perform experiments on three authentic datasets, showcasing its notable advantages.