IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i15p3393-d1209863.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/15/3393/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/15/3393/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hui Li & Lanlan Jiang & Jun Li, 2024. "Continuous-Time Dynamic Graph Networks Integrated with Knowledge Propagation for Social Media Rumor Detection," Mathematics, MDPI, vol. 12(22), pages 1-17, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lingnan He & Haoshen Yang & Xiling Xiong & Kaisheng Lai, 2019. "Online Rumor Transmission Among Younger and Older Adults," SAGE Open, , vol. 9(3), pages 21582440198, September.
    2. Lian, Ying & Liu, Yijun & Dong, Xuefan, 2020. "Strategies for controlling false online information during natural disasters: The case of Typhoon Mangkhut in China," Technology in Society, Elsevier, vol. 62(C).
    3. Wingyan Chung & Yinqiang Zhang & Jia Pan, 2023. "A Theory-based Deep-Learning Approach to Detecting Disinformation in Financial Social Media," Information Systems Frontiers, Springer, vol. 25(2), pages 473-492, April.
    4. Xiaohui Zhang & Qianzhou Du & Zhongju Zhang, 2022. "A theory‐driven machine learning system for financial disinformation detection," Production and Operations Management, Production and Operations Management Society, vol. 31(8), pages 3160-3179, August.
    5. Abderrazek Azri & Cécile Favre & Nouria Harbi & Jérôme Darmont & Camille Noûs, 2023. "Rumor Classification through a Multimodal Fusion Framework and Ensemble Learning," Information Systems Frontiers, Springer, vol. 25(5), pages 1795-1810, October.
    6. Jyoti Prakash Singh & Abhinav Kumar & Nripendra P. Rana & Yogesh K. Dwivedi, 2022. "Attention-Based LSTM Network for Rumor Veracity Estimation of Tweets," Information Systems Frontiers, Springer, vol. 24(2), pages 459-474, April.
    7. Bei Bi & Yaojun Wang & Haicang Zhang & Yang Gao, 2022. "Microblog-HAN: A micro-blog rumor detection model based on heterogeneous graph attention network," PLOS ONE, Public Library of Science, vol. 17(4), pages 1-18, April.
    8. Zhu, He & Ma, Jing & Li, Shan, 2019. "Effects of online and offline interaction on rumor propagation in activity-driven networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1124-1135.
    9. Serveh Lotfi & Mitra Mirzarezaee & Mehdi Hosseinzadeh & Vahid Seydi, 2021. "Rumor conversations detection in twitter through extraction of structural features," Information Technology and Management, Springer, vol. 22(4), pages 265-279, December.
    10. Kathrin Eismann, 2021. "Diffusion and persistence of false rumors in social media networks: implications of searchability on rumor self-correction on Twitter," Journal of Business Economics, Springer, vol. 91(9), pages 1299-1329, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:15:p:3393-:d:1209863. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.