Dynamic Simulation Framework for Disinformation Dissemination and Correction With Social Bots

TL;DR

This paper introduces MADD, a comprehensive simulation framework that models disinformation spread and correction in social networks with realistic dynamics and structures, enabling better analysis of intervention strategies.

Contribution

MADD is the first framework to incorporate dynamic bot behavior, realistic network topology, and quantitative evaluation of correction strategies in disinformation dissemination modeling.

Findings

MADD accurately replicates real-world user attributes and network structures.

Simulation of disinformation topics reveals differential impacts of correction strategies.

Quantitative analysis enables assessment of intervention effectiveness.

Abstract

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.