Large Language Model-driven Multi-Agent Simulation for News Diffusion Under Different Network Structures

TL;DR

This paper presents an innovative LLM-driven multi-agent simulation framework to study news diffusion and misinformation spread across different network structures, offering insights into effective countermeasures.

Contribution

It introduces a novel LLM-based multi-agent simulation approach that models complex information ecosystem interactions and evaluates misinformation mitigation strategies.

Findings

LLM-based agents better model complex information dynamics.

Countermeasures like blocking influential agents can reduce misinformation.

Network structure significantly affects countermeasure effectiveness.

Abstract

The proliferation of fake news in the digital age has raised critical concerns, particularly regarding its impact on societal trust and democratic processes. Diverging from conventional agent-based simulation approaches, this work introduces an innovative approach by employing a large language model (LLM)-driven multi-agent simulation to replicate complex interactions within information ecosystems. We investigate key factors that facilitate news propagation, such as agent personalities and network structures, while also evaluating strategies to combat misinformation. Through simulations across varying network structures, we demonstrate the potential of LLM-based agents in modeling the dynamics of misinformation spread, validating the influence of agent traits on the diffusion process. Our findings emphasize the advantages of LLM-based simulations over traditional techniques, as they uncover underlying causes of information spread -- such as agents promoting discussions -- beyond the predefined rules typically employed in existing agent-based models. Additionally, we evaluate three countermeasure strategies, discovering that brute-force blocking influential agents in the network or announcing news accuracy can effectively mitigate misinformation. However, their effectiveness is influenced by the network structure, highlighting the importance of considering network structure in the development of future misinformation countermeasures.