Abstract
With the rapid development of network media, it becomes easier to spread rumors, which makes the research on rumor detection more urgent. The current automatic rumor detection methods focus on modeling by artificial features, which has a low accuracy rate and lacks comprehensive consideration. So many deep learning methods are applied to discover rumors through graph convolution network. However, these methods only take into account the patterns of propagation and dispersion but ignore the information user in rumor detection. In this paper, we propose a model of rumor detection based on graph convolution network, constructs User graph and post graph of rumor propagation and dispersion. The user attribute and post information are modeled by graph volume network, and the user representation and post representation are obtained respectively. By pooling and full connection, the detection is finally realized. Rumor detection by combining the outputs of the above three modules. The experimental results show that the accuracy of this method is significantly improved in rumor detection.
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Ge, X., Zhang, M., Wei, B., Liu, Y. (2022). A Rumor Detection Method Based on Graph Convolutional Network. In: Li, X. (eds) Advances in Intelligent Automation and Soft Computing. IASC 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 80. Springer, Cham. https://doi.org/10.1007/978-3-030-81007-8_47
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DOI: https://doi.org/10.1007/978-3-030-81007-8_47
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