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Rumor detection based on a Source-Replies conversation Tree Convolutional Neural Net

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Abstract

Rumor detection is a hot research topic in social networks. It is challenging to simultaneously extract content features and structural features from rumor conversations to detect rumors, and many existing methods only focus on one of them. In this paper, we propose a Source-Replies conversation Tree Convolutional Neural Net (TCN) to extract these two features simultaneously for the rumor detection task. Specifically, we first build Source-Replies conversation Trees (SR-Trees) based on rumor conversations, and then we construct an SR-Tree-based Auto-Encoder (TAE) on SR-Trees. A TAE designs Spatial Tree Convolution and Tree-pooling to build an effective feature extractor to extract content features and structural features from SR-Trees. Based on the pre-trained feature extractor in the TAE, an end-to-end TCN is proposed to detect rumors. In experiments, we verify that compared with other commonly used Rumor analysis models, the proposed TCN is effective on the rumor detection task.

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Acknowledgements

This work was supported by the Special Funds for Innovation of Graduate Students Double Top University Plain in China University of Mining and Technology (No. 2018ZZCX14).

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China

    Na Bai, Fanrong Meng, Xiaobin Rui & Zhixiao Wang

  2. Mine Digitization Energineering Research Center of the Ministry of Education, Xuzhou, 221116, Jiangsu, China

    Fanrong Meng & Zhixiao Wang

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  1. Na Bai
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  2. Fanrong Meng
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  3. Xiaobin Rui
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  4. Zhixiao Wang
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Correspondence to Fanrong Meng or Zhixiao Wang.

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Bai, N., Meng, F., Rui, X. et al. Rumor detection based on a Source-Replies conversation Tree Convolutional Neural Net. Computing 104, 1155–1171 (2022). https://doi.org/10.1007/s00607-021-01034-5

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