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<p>To explore the impact of various groups and methods on rumor propagation, the authors propose a ‘Double-Refutation (<i>DR</i>) and Double-Blocking (<i>DB</i>)’ rumor control strategy. This strategy combines external refutation via media reports, internal refutation by counteracting individuals, and both continuous and impulse blocking methods. By leveraging multi-synergy and aiming to minimize control costs, the authors propose stochastic optimal hybrid control strategies for rumor containment. Additionally, to enhance the response speed of the control strategy, the authors introduce an ensemble learning algorithm as a substitute for theoretical solutions. Numerical simulations demonstrate that the trained ensemble learning control algorithm can quickly identify sub-optimal control strategies for rumor spreading, with costs only 4.1% higher than those of the optimal control theory.</p>

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Stochastic Hybrid Rumor Control: A Data-Driven Ensemble Learning Control Algorithm

  • 期刊论文

  • 发表日期：

    2025年9月2日

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  • Xiaojing Zhong,
  • Jiaxin Zeng,
  • Wendi Xiang,
  • Tomás Caraballo,
  • Feiqi Deng,
  • Yuqing Peng

摘要

To explore the impact of various groups and methods on rumor propagation, the authors propose a ‘Double-Refutation (DR) and Double-Blocking (DB)’ rumor control strategy. This strategy combines external refutation via media reports, internal refutation by counteracting individuals, and both continuous and impulse blocking methods. By leveraging multi-synergy and aiming to minimize control costs, the authors propose stochastic optimal hybrid control strategies for rumor containment. Additionally, to enhance the response speed of the control strategy, the authors introduce an ensemble learning algorithm as a substitute for theoretical solutions. Numerical simulations demonstrate that the trained ensemble learning control algorithm can quickly identify sub-optimal control strategies for rumor spreading, with costs only 4.1% higher than those of the optimal control theory.