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<p>We propose a resilient framework for the mitigation of misinformation epidemics within dynamic information ecosystems under operational latencies and adversarial telemetry corruption. A dual-layer control architecture that balances platform-level usability with hard safety constraints is designed. The framework utilizes a polyhedral backward induction scheme to synthesize a verified controlled invariant cover. This geometric formulation guarantees that node-level infodemic penetration levels remain strictly bounded within a designated safe target set. To counter coordinated false data injection (FDI) attacks on state reporting channels, we integrate an online state observer, utilizing a private physical watermarking sequence, <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\Delta w(t)\)</EquationSource> </InlineEquation>. This mechanism creates an asymmetric information structure that exposes stealthy evasion tactics through a Chi-Squared (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\chi ^2\)</EquationSource> </InlineEquation>) tracking residual monitor. Parametric sensitivity profiling maps the operational boundaries of the network, isolating the primary destabilizing role of virality (<InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\beta \)</EquationSource> </InlineEquation>) alongside the primary stabilizing lever of intervention effectiveness (<InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(\kappa \)</EquationSource> </InlineEquation>). Empirical validation conducted on synthetic networks and the ESOC COVID-19 Misinformation Dataset demonstrates that the self-triggered adaptive control law consistently outperforms baseline implementations, yielding a platform usability cost reduction between <InlineEquation ID="IEq5"> <EquationSource Format="TEX">\(38.5\%\)</EquationSource> </InlineEquation> and <InlineEquation ID="IEq6"> <EquationSource Format="TEX">\(53.8\%\)</EquationSource> </InlineEquation> while maintaining absolute safety integrity. These results establish the framework as a robust tool for securing critical information infrastructure against sophisticated, coordinated manipulation.</p>

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Adaptive networked control of misinformation epidemics: safety and usability trade-offs with resilient estimation

  • 期刊论文

  • 发表日期：

    2026年7月31日

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  • Mordecai Opoku Ohemeng,
  • Frederick T. Sheldon

摘要

We propose a resilient framework for the mitigation of misinformation epidemics within dynamic information ecosystems under operational latencies and adversarial telemetry corruption. A dual-layer control architecture that balances platform-level usability with hard safety constraints is designed. The framework utilizes a polyhedral backward induction scheme to synthesize a verified controlled invariant cover. This geometric formulation guarantees that node-level infodemic penetration levels remain strictly bounded within a designated safe target set. To counter coordinated false data injection (FDI) attacks on state reporting channels, we integrate an online state observer, utilizing a private physical watermarking sequence, \(\Delta w(t)\) . This mechanism creates an asymmetric information structure that exposes stealthy evasion tactics through a Chi-Squared ( \(\chi ^2\) ) tracking residual monitor. Parametric sensitivity profiling maps the operational boundaries of the network, isolating the primary destabilizing role of virality ( \(\beta \) ) alongside the primary stabilizing lever of intervention effectiveness ( \(\kappa \) ). Empirical validation conducted on synthetic networks and the ESOC COVID-19 Misinformation Dataset demonstrates that the self-triggered adaptive control law consistently outperforms baseline implementations, yielding a platform usability cost reduction between \(38.5\%\) and \(53.8\%\) while maintaining absolute safety integrity. These results establish the framework as a robust tool for securing critical information infrastructure against sophisticated, coordinated manipulation.