Graph Convolutional-Based Deep Residual Modeling for Rumor Detection on Social Media

Na Ye, Dingguo Yu (), Yijie Zhou, Ke-ke Shang () and Suiyu Zhang
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Na Ye: School of Journalism and Communication, Communication University of Zhejiang, Hangzhou 310018, China
Dingguo Yu: College of Media Engineering, Communication University of Zhejiang, Hangzhou 310018, China
Yijie Zhou: Key Lab of Film and TV Media Technology of Zhejiang Province, Hangzhou 310018, China
Ke-ke Shang: Computational Communication Collaboratory, Nanjing University, Nanjing 210023, China
Suiyu Zhang: Key Lab of Film and TV Media Technology of Zhejiang Province, Hangzhou 310018, China

Mathematics, 2023, vol. 11, issue 15, 1-11

Abstract: The popularity and development of social media have made it more and more convenient to spread rumors, and it has become especially important to detect rumors in massive amounts of information. Most of the traditional rumor detection methods use the rumor content or propagation structure to mine rumor characteristics, ignoring the fusion characteristics of the content and structure and their interaction. Therefore, a novel rumor detection method based on heterogeneous convolutional networks is proposed. First, this paper constructs a heterogeneous map that combines both the rumor content and propagation structure to explore their interaction during rumor propagation and obtain a rumor representation. On this basis, this paper uses a deep residual graph convolutional neural network to construct the content and structure interaction information of the current network propagation model. Finally, this paper uses the Twitter15 and Twitter16 datasets to verify the proposed method. Experimental results show that the proposed method has higher detection accuracy compared to the traditional rumor detection method.

Keywords: false information detection; residual structure; graph neural network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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