Methodology of multi-agent system for misinformation and disinformation detection - Melnychuk, O.V. (Problems of Informatization and Management, 2025)
The rapid growth of generative artificial intelligence has led to an unprecedented surge in disinformation, significantly complicating traditional media monitoring methods and necessitating the implementation of new automated verification tools. However, the effectiveness of existing manipulation and disinformation detection systems often relies on powerful commercial Large Language Models (LLMs), which entail high operational costs, limited accessibility, and risks associated with transferring confidential data to external servers. This paper assesses the potential of utilizing Small Language Models (SLMs) as a foundation for building autonomous systems to counter information manipulation. A proposed approach to constructing a verification system based on multi-agent interaction is presented, in which a complex task is decomposed into subtasks for specialized agents: a manipulation detector, a narrative analyst, and a fact-checker. The results of an experimental comparison of local and cloud-based models indicate that the proposed approach is more secure and cost-effective, achieving high analysis accuracy while maintaining full control over user data.