An important aspect of preventing fake news dissemination is to proactively detect the likelihood of its spreading. Research in the domain of fake news spreader detection has not been explored much from a network analysis perspective. In this paper, we propose a graph neural network based approach to identify nodes that are likely to become spreaders of false information. Using the community health assessment model and interpersonal trust we propose an inductive representation learning framework to predict nodes of densely-connected community structures that are most likely to spread fake news, thus making the entire community vulnerable to the infection. Using topology and interaction based trust properties of nodes in real-world Twitter networks, we are able to predict false information spreaders with an accuracy of over 90%.

中文翻译：

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使用归纳表示学习检测社交网络中的虚假新闻传播者

防止虚假新闻传播的一个重要方面是主动检测其传播的可能性。从网络分析的角度出发，对伪造新闻传播器检测领域的研究尚未进行过多的研究。在本文中，我们提出了一种基于图神经网络的方法来识别可能成为错误信息传播者的节点。使用社区健康评估模型和人际信任，我们提出了一种归纳表示学习框架，以预测紧密连接的社区结构的节点，这些节点最有可能传播假新闻，从而使整个社区容易受到感染。使用真实的Twitter网络中节点的基于拓扑和交互的信任属性，我们能够以90％以上的准确性预测错误的信息传播者。