构建动态仿真框架，模拟虚假信息传播与纠正效果。
Dynamic Simulation Framework for Disinformation Dissemination and Correction With Social Bots
- 基于多智能体建模，融合真实用户数据与复杂网络结构。
- 验证了事实型与叙事型纠错策略在六类虚假信息中的差异效果。
- 适合研究虚假信息治理、社交机器人影响的政策与技术团队。
在人机协同的信息生态系统中，社交机器人在虚假信息的传播与纠正中扮演关键角色。理解其影响对风险控制与治理至关重要。然而，现有研究常依赖简化的用户与网络建模，忽视机器人的动态行为，且缺乏对纠正策略的量化评估。为此，我们提出MADD（多智能体虚假信息传播框架），通过整合巴尔-阿尔伯特模型构建无标度拓扑，结合随机块模型刻画社区结构，并基于真实用户数据设计节点属性，使传播网络更贴近现实。MADD同时包含恶意与合法机器人，通过可控的动态参与机制，实现对纠正策略的定量分析。我们采用个体与群体级指标评估框架，实验验证了用户属性与网络结构在真实世界的一致性，并模拟了六类虚假信息的传播过程，揭示了基于事实与基于叙事的纠正策略的差异化效果。
原文摘要 · Abstract (English)
In the human-bot symbiotic information ecosystem, social bots play key roles in spreading and correcting disinformation. Understanding their influence is essential for risk control and better governance. However, current studies often rely on simplistic user and network modeling, overlook the dynamic behavior of bots, and lack quantitative evaluation of correction strategies. To fill these gaps, we propose MADD, a Multi Agent based framework for Disinformation Dissemination. MADD constructs a more realistic propagation network by integrating the Barabasi Albert Model for scale free topology and the Stochastic Block Model for community structures, while designing node attributes based on real world user data. Furthermore, MADD incorporates both malicious and legitimate bots, with their controlled dynamic participation allows for quantitative analysis of correction strategies. We evaluate MADD using individual and group level metrics. We experimentally verify the real world consistency of MADD user attributes and network structure, and we simulate the dissemination of six disinformation topics, demonstrating the differential effects of fact based and narrative based correction strategies.
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