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Multi-stage dynamic disinformation detection with graph entropy guidance

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Abstract

Online disinformation has become one of the most severe concerns in today’s world. Recognizing disinformation timely and effectively is very hard, because the propagation process of disinformation is dynamic and complicated. The existing newest research leverage uniform time intervals to study the multi-stage propagation features of disinformation. However, uniform time intervals are unrealistic in the real world, cause the process of information propagation is not regular. In light of these facts, we propose a novel and effective framework Multi-stage Dynamic Disinformation Detection with Graph Entropy Guidance(MsDD) to better analyze multi-stage propagation patterns. Instead of traditional snapshots, we analyze the dynamic propagation network via graph entropy, which can work effectively in finding the dynamic and variable-length stages. In this way, we can explicitly learn the changing pattern of propagation stages and support timely detection even at the early stages. Based on this effective multi-stage analysis framework, we further propose a novel dynamic analysis model to model both the structural and sequential evolving features. Extensive experiments on two real-world datasets prove the superiority of our model. We open the datasets and source code at https://github.com/researchxr/MsDD.

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Availability of Supporting Data

All data that support the findings of this study are openly available. The Pheme datasets are included in the published article [4]. The MisInfdect datasets are available at https://weibo.com/weibopiyao.

Notes

  1. https://weibo.com/weibopiyao

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Acknowledgements

This work is supported by National Key R &D Program of China under Grants No. 2022YFB3104300. National Natural Science Foundation of China under Grants No. 61972087. Jiangsu Provincial Key Laboratory of Network and Information Security under Grants No. BM2003201, and Key Laboratory of Computer Network and Information Integration of Ministry of Education of China under Grants No. 93K-9.

Funding

This work is supported by National Key R &D Program of China under Grants No. 2022YFB3104300. National Natural Science Foundation of China under Grants No. 61972087. Jiangsu Provincial Key Laboratory of Network and Information Security under Grants No. BM2003201, and Key Laboratory of Computer Network and Information Integration of Ministry of Education of China under Grants No. 93K-9.

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Authors and Affiliations

  1. Southeast University, Nanjing, China

    Xiaorong Hao, Bo Liu, Xinyan Yang & Jiuxin Cao

  2. Purple Mountain Laboratories, Nanjing, China

    Bo Liu

  3. The Chinese University of Hong Kong, Hong Kong SAR, China

    Xiangguo Sun

  4. Hohai University, Nanjing, China

    Qing Meng

  5. Sanjiang University, Nanjing, China

    Jiuxin Cao

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  1. Xiaorong Hao
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  2. Bo Liu
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  3. Xinyan Yang
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  4. Xiangguo Sun
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  5. Qing Meng
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  6. Jiuxin Cao
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Contributions

Xiaorong Hao: Methodology, Formal analysis, Visualization, Writing - Original draft, Writing - Review & editing. Bo Liu: Supervision, Conceptualization, Writing - Review & editing. Xinyan Yang: Methodology, Validation, Visualization, Data curation. Xiangguo Sun: Writing - Review & editing. Qing Meng: Writing - Review & editing. Jiuxin Cao: Writing - Review & editing.

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Correspondence to Bo Liu.

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This article belongs to the Topical Collection: Special Issue on Privacy and Security in Machine Learning

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Hao, X., Liu, B., Yang, X. et al. Multi-stage dynamic disinformation detection with graph entropy guidance. World Wide Web 27, 8 (2024). https://doi.org/10.1007/s11280-024-01243-w

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