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

Author

Listed:
  • 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
    Key Lab of Film and TV Media Technology of Zhejiang Province, Hangzhou 310018, China)

  • Yijie Zhou

    (Key Lab of Film and TV Media Technology of Zhejiang Province, Hangzhou 310018, China
    Institute of Intelligent Media Technology, Communication University of Zhejiang, 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)

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.

Suggested Citation

  • Na Ye & Dingguo Yu & Yijie Zhou & Ke-ke Shang & Suiyu Zhang, 2023. "Graph Convolutional-Based Deep Residual Modeling for Rumor Detection on Social Media," Mathematics, MDPI, vol. 11(15), pages 1-11, August.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:15:p:3393-:d:1209863
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    References listed on IDEAS

    as
    1. Sejeong Kwon & Meeyoung Cha & Kyomin Jung, 2017. "Rumor Detection over Varying Time Windows," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-19, January.
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