Abstract
With the rapid development of the Internet, social media has become the main platform for users to obtain news and share their opinions. While social media provides convenience to the life of people, it also offers advantageous conditions for publishing and spreading rumors. Since artificial detection methods take a lot of time, it becomes crucial to use intelligent methods for rumor detection. The recent rumor detection methods mostly use the meta-paths of post propagation to construct isomorphic graphs to find clues in the propagation structure. However, these methods do not fully use the global and local relations in the propagation graph and do not consider the correlation between different types of nodes. In this paper, we propose a Global-Local Relations Encoding Network (GLREN), which encodes node relations in the heterogeneous graph from global and local perspectives. First, we explore the semantic similarity between all source posts and comment posts to generate global and local semantic representations. Then, we construct user credibility levels and interaction relations to explore the potential relationship between users and misinformation. Finally, we introduce a root enhancement strategy to enhance the influence of source posts and publisher information. The experimental results show that our model can outperform the accuracy of the state-of-the-art methods by 3.0% and 6.0% on Twitter15 and Twitter16, respectively.
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Zhang, X., Pan, S., Qian, C., Yuan, J. (2023). Rumor Detection on Social Media by Using Global-Local Relations Encoding Network. In: Dang-Nguyen, DT., et al. MultiMedia Modeling. MMM 2023. Lecture Notes in Computer Science, vol 13834. Springer, Cham. https://doi.org/10.1007/978-3-031-27818-1_44
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DOI: https://doi.org/10.1007/978-3-031-27818-1_44
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