Dynamic Simulation Framework for Disinformation Dissemination and Correction With Social Bots
This work addresses the need for better simulation tools to analyze disinformation risks and correction strategies, though it is incremental in improving modeling realism over prior simplistic approaches.
The authors tackled the problem of understanding social bots' roles in disinformation spread and correction by proposing MADD, a multi-agent simulation framework that integrates realistic network models and bot dynamics, showing differential effects of fact-based and narrative-based correction strategies across six disinformation topics.
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.