Dynamic Simulation Framework for Disinformation Dissemination and Correction With Social Bots (2025)

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 Barabási–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’s 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. Our code is publicly available at https://github.com/QQQQQQBY/BotInfluence .

## introduction in the era of human-bot symbiosis, social bots are prevalent on social networks and actively involved in information sharing @xcite . malicious bots are strategically deployed to spread disinformation and disrupt healthy online discussions @xcite . conversely, legitimate bots are increasingly employed for the task of disinformation correction @xcite . therefore, developing robust network propagation simulation frameworks that quantitatively analyze the interaction between these opposing bots in information dissemination can help us better govern disinformation. prior work in simulating disinformation propagation mainly follows two main paradigms: digital network-based propagation dynamics modeling and large language model (llm)-based agent simulation. as shown in figure 1 (a), digital networkbased models @xcite @xcite simulate information diffusion through user state transitions. although computationally efficient, this conventional approach oversimplifies reality by ignoring user demographics and individual behavioral differences, limiting its ability to capture key phenomena such as repeated exposure and group polarization. figure 1(b ) illustrates the emerging llm-based agent simulation paradigm @xcite , which improves user behavior modeling through semantic features and opinion propagating. however, it still faces notable limitations, redundant and irrelevant user attributes interfere with core propagation modeling, while inaccurate reconstruction of real-world network topologies causes deviations from actual scenarios. furthermore, current simulation research on social bots' involvement in disinformation dissemination and correction still faces significant limitations @xcite . for example, while @xcite simulate bot-driven disinformation spread, they neglect the construction of intervention scenarios and lack systematic effectiveness evaluation. these shortcomings highlight the urgent need for a more comprehensive simulation framework that integrates core demographic features, realistic propagating network topologies, and a robust evaluation metrics, thereby enhancing the authenticity of dissemination modeling. to address the aforementioned limitations, we propose madd, a multi-agent-based framework for disinformation dissemination, designed to model both the dissemination and correction of disinformation by social bots. madd integrates the scale-free property of barabási-albert model (bam, @xcite ) and the community structure of stochastic block model (sbm @xcite ) to construct a more realistic propagation network. it includes five node attributes, carefully designed based on real-world user data. furthermore, we design three types of agents: regular users, malicious bots (spreading disinformation), and legitimate bots (correcting disinformation), enabling dynamic interactions. using group and individual-level evaluation metrics, we validate the framework's effectiveness across six diverse disinformation topics and demonstrate the practical efficacy of fact-based and narrative-based correction strategies. our study contributes the following: ## methodology in this work, we design a multi-agent-based framework for disinformation dissemination (madd) to model the dynamic impact of social bots on disinformation dissemination and correction. as shown in figure 2 , madd comprises three modules: disinformation dissemination modeling, correction strategy, and propagation impact quantification.