Graph Convolutional Network-Based Rumor Blocking on Social Networks

谣言 阻塞(统计) 误传 计算机科学 图形 社交网络(社会语言学) 社会化媒体 微博 理论计算机科学 计算机安全 万维网 计算机网络 政治学 公共关系
作者
Qiang He,Dafeng Zhang,Xingwei Wang,Lianbo Ma,Yong Zhao,Fei Gao,Min Huang
出处
期刊:IEEE Transactions on Computational Social Systems [Institute of Electrical and Electronics Engineers]
卷期号:10 (5): 2244-2253 被引量:6
标识
DOI:10.1109/tcss.2022.3188701
摘要

Misinformation and rumors can spread rapidly and widely through online social networks, seriously endangering social stability. Therefore, rumor blocking on social networks has become a hot research topic. In the existing research, when users receive two opposing opinions, they tend to believe the one arrives first. In this article, we argue that users will dialectically trust the information based on their own opinions rather than the rule of first-come-first-listen. We propose a confidence-based opinion adoption (CBOA) model, which considers the opinion and confidence according to the traditional linear threshold (LT) model. Based on this model, we propose the directed graph convolutional network (DGCN) method to select the $k$ most influential positive cascade nodes to suppress the propagation of rumors. Finally, we verify our method on four real network datasets. The experimental results show that our method can sufficiently suppress the propagation of rumors and obtains smaller number of rumor nodes than the baseline algorithms.
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