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Combining Temporal and Interactive Features for Rumor Detection: A Graph Neural Network Based Model

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Abstract

Social media has been developing rapidly and brings along several advantages. However, information spreading on social media with an unverified status, which tends to be rumors later becomes a potential threat. Researchers have made many attempts to detect rumors with machine learning or deep learning methods. But the existing research did not simultaneously consider the temporal and interactive features, which are two crucial features of rumors. In this paper, we proposed a novel Graph Neural Network based model with temporal and interactive features for rumors detection. We leverage Discrete Fourier Transform to convert the variation of the post’s number related to a specific social event to the frequency domain to obtain the temporal feature of the rumor, and we apply the Fast Fourier Transform algorithm to reduce the amount of calculation. Then we build a rumor interactive graph based on the reply and repost relationships and we use multiple independent Graph Attention Network modules to extract interactive features. Finally, the temporal and interactive features are combined to make the classification. Encouraging empirical results on the Sina Weibo dataset and PHEME dataset confirm the superiority of the proposed method over the state-of-the-art approaches.

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Notes

  1. https://scikit-learn.org.

  2. https://pytorch.org.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61601046 and Grant 61171098, in part by the 111 Project of China under Grant B08004, in part by the EU FP7 IRSES Mobile Cloud Project under Grant 12212.

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  1. The School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Song Han, Ke Yu, Xing Su & Xiaofei Wu

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  1. Song Han
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  2. Ke Yu
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Correspondence to Ke Yu.

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Han, S., Yu, K., Su, X. et al. Combining Temporal and Interactive Features for Rumor Detection: A Graph Neural Network Based Model. Neural Process Lett 55, 5675–5691 (2023). https://doi.org/10.1007/s11063-022-11105-z

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