A model for early rumor detection base on topic-derived domain compensation and multi-user association

Abstract

In the era of rapid Internet development, social networking sites have become breeding grounds for rumors. Detecting and addressing these rumors early is crucial to mitigate the potentially devastating consequences they may bring. This paper presents an innovative model for early rumor detection to tackle the challenge posed by sparse data in the early stages of rumor outbreaks. Firstly, a content-based method for data compensation is proposed, considering the feature correlation between the original and derived topics. The method effectively matches derived topics based on the content features of the topics, thereby enriching the original domain and solving the challenge of data sparsity. Secondly, a method for constructing the network structure is proposed to tackle the issue of user association within the early topic domain. This method introduces a quantification approach to evaluate the association relationship among multiple users. By incorporating game theory to quantify user emotion, the method effectively transforms the topic domain into a multi-user relationship network with a non-Euclidean structure. Finally, leveraging the capabilities of Graph Convolutional Networks (GCN) to process non-euclidean data, a novel early rumor detection model, namely DDCA-GCN, is proposed. It combines the strategies of topic-derived domain compensation and the principle of multi-user correlation. It can effectively identify early rumors and simultaneously comprehensively analyze users’ responses. The experimental results successfully demonstrate the feasibility of incorporating derived topic elements for early rumor detection. This validation underscores the effectiveness of leveraging derived topic domains to bolster the accuracy and timeliness of rumor detection.