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Minimum budget for misinformation blocking in online social networks

Author

Listed:
  • Canh V. Pham

    (University of Engineering and Technology, Vietnam National University
    People’s Security Academy)

  • Quat V. Phu

    (People’s Security Academy)

  • Huan X. Hoang

    (University of Engineering and Technology, Vietnam National University)

  • Jun Pei

    (Hefei University of Technology)

  • My T. Thai

    (University of Florida)

Abstract

Preventing misinformation spreading has recently become a critical topic due to an explosive growth of online social networks. Instead of focusing on blocking misinformation with a given budget as usually studied in the literatures, we aim to find the smallest set of nodes (minimize the budget) whose removal from a social network reduces the influence of misinformation (influence reduction) greater than a given threshold, called the Targeted Misinformation Blocking problem. We show that this problem is #P-hard under Linear Threshold and NP-hard under Independent Cascade diffusion models. We then propose several efficient algorithms, including approximation and heuristic algorithms to solve the problem. Experiments on real-world network topologies show the effectiveness and scalability of our algorithms that outperform other state-of-the-art methods.

Suggested Citation

  • Canh V. Pham & Quat V. Phu & Huan X. Hoang & Jun Pei & My T. Thai, 2019. "Minimum budget for misinformation blocking in online social networks," Journal of Combinatorial Optimization, Springer, vol. 38(4), pages 1101-1127, November.
    • Handle: RePEc:spr:jcomop:v:38:y:2019:i:4:d:10.1007_s10878-019-00439-5
    DOI: 10.1007/s10878-019-00439-5
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    References listed on IDEAS

    as
    1. Canh V. Pham & My T. Thai & Hieu V. Duong & Bao Q. Bui & Huan X. Hoang, 2018. "Maximizing misinformation restriction within time and budget constraints," Journal of Combinatorial Optimization, Springer, vol. 35(4), pages 1202-1240, May.
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    Cited by:

    1. Wingyan Chung & Yinqiang Zhang & Jia Pan, 2023. "A Theory-based Deep-Learning Approach to Detecting Disinformation in Financial Social Media," Information Systems Frontiers, Springer, vol. 25(2), pages 473-492, April.

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