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<p>In the digital age, social networks have become critical platforms for information dissemination, but they also pose significant risks due to the rapid spread of rumors and misinformation. Existing approaches to rumor control often rely on models that assume a single exposure to anti-rumor information is sufficient to mitigate its impact, overlooking the necessity of multiple impressions for effective behavior change. In this work, we address the Rumor Control with Impression Counts problem by proposing the first-ever Machine Learning (ML)-based solution. Our approach leverages Graph Neural Networks combined with a greedy algorithm to efficiently manage large-scale social networks. To further enhance computational efficiency, we incorporate Evolutionary Optimization, resulting in a method that not only addresses the effectiveness challenges but also scales efficiently with network size. Extensive experiments on real-world datasets demonstrate that our approach outperforms existing methods in both effectiveness and scalability, improving computational efficiency by 1 to 2 orders of magnitude.</p>

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Proactive Rumor Control Using Graph Neural Networks and Evolutionary Optimization

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月22日

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  • Pengfei Xu,
  • Liwei Wang,
  • Zhiyong Peng

摘要

In the digital age, social networks have become critical platforms for information dissemination, but they also pose significant risks due to the rapid spread of rumors and misinformation. Existing approaches to rumor control often rely on models that assume a single exposure to anti-rumor information is sufficient to mitigate its impact, overlooking the necessity of multiple impressions for effective behavior change. In this work, we address the Rumor Control with Impression Counts problem by proposing the first-ever Machine Learning (ML)-based solution. Our approach leverages Graph Neural Networks combined with a greedy algorithm to efficiently manage large-scale social networks. To further enhance computational efficiency, we incorporate Evolutionary Optimization, resulting in a method that not only addresses the effectiveness challenges but also scales efficiently with network size. Extensive experiments on real-world datasets demonstrate that our approach outperforms existing methods in both effectiveness and scalability, improving computational efficiency by 1 to 2 orders of magnitude.