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RumorMixer: Exploring Echo Chamber Effect and Platform Heterogeneity for Rumor Detection

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Part of the book series:Lecture Notes in Computer Science ((LNAI,volume 14941))

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Abstract

Rumors have exerted detrimental effects on individuals and societies in recent years. Despite the deployment of sophisticated Graph Neural Networks (GNNs) to analyze the structure of propagation graphs in rumor detection, contemporary approaches often neglect two pivotal elements. Firstly, the structure of rumor propagation in social networks is characterized by a community-based feature, influenced by the “echo chamber effect”. By integrating these structures, models can emphasize critical information, mitigate the impact of irrelevant data, and enhance graph representation learning. Secondly, the existing models for rumor detection struggle to adjust GNN backbones to accommodate the diverse complexities introduced by social media’s platform heterogeneity. The manual design of these models is both time-consuming and labor-intensive. To overcome these challenges, this paper presentsRumorMixer, a novel automated framework for rumor detection. This methodology begins by developing a Super-Sharer-Aware (SSA) chamber partitioning algorithm, crucial for identifying echo chambers within propagation graphs. Through accurate partitioning, RumorMixer effectively concentrates on the essential structures of rumor propagation and utilizes the GNN-Mixer model to create high-quality representations of these chambers. To address platform heterogeneity, RumorMixer integrates five distinct components:PE,Aggregation,Merge,Pooling, andMixing-to establish an extensive search space. Subsequently, differentiable architecture search technology is employed to automatically tailor platform-specific architectures. The efficacy is validated through extensive experiments on real datasets from both Weibo and Twitter3(Our code is accessible athttps://github.com/cgao-comp/RumorMixer.).

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Acknowledgements

We thank the anonymous reviewers for their valuable comments. This work was supported in part by the National Natural Science Foundation of China (Nos. U22B2036, 62271411, U22A2098, 11931015, 61976181), the National Science Fund for Distinguished Young Scholars (No. 62025602), Fok Ying-Tong Education Foundationm China (No. 171105), and the XPLORER PRIZE.

Author information

Authors and Affiliations

  1. School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, People’s Republic of China

    Haowei Xu, Chao Gao & Xianghua Li

  2. School of Cybersecurity, Northwestern Polytechnical University, Shaanxi, People’s Republic of China

    Zhen Wang

Authors
  1. Haowei Xu

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  2. Chao Gao

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  3. Xianghua Li

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  4. Zhen Wang

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Corresponding author

Correspondence toXianghua Li.

Editor information

Editors and Affiliations

  1. LTCI, Télécom Paris, Palaiseau Cedex, France

    Albert Bifet

  2. KU Leuven, Leuven, Belgium

    Jesse Davis

  3. Faculty of Informatics, Vytautas Magnus University, Akademija, Lithuania

    Tomas Krilavičius

  4. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Meelis Kull

  5. Department of Computer Science, Bundeswehr University Munich, Munich, Germany

    Eirini Ntoutsi

  6. Department of Computer Science, University of Helsinki, Helsinki, Finland

    Indrė Žliobaitė

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Xu, H., Gao, C., Li, X., Wang, Z. (2024). RumorMixer: Exploring Echo Chamber Effect and Platform Heterogeneity for Rumor Detection. In: Bifet, A., Davis, J., Krilavičius, T., Kull, M., Ntoutsi, E., Žliobaitė, I. (eds) Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2024. Lecture Notes in Computer Science(), vol 14941. Springer, Cham. https://doi.org/10.1007/978-3-031-70341-6_2

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