APPLICATION OF IMPROVED KURAMOTO MODELS FOR IDENTIFYING DISINFORMATION IN SOCIAL NETWORKS

This article examines the problem of disinformation spreading in social networks and proposes advanced approaches for its identification based on improved Kuramoto models. In the modern world, social networks have become powerful communication tools that enable the rapid exchange of information among millions of people. At the same time, these networks facilitate the dissemination of disinformation — deliberately false or manipulative information used to influence public opinion and achieve political, social, or economic goals. The increasing scale of disinformation poses a serious threat to information security, societal stability, and trust in state institutions. To address this issue, the article proposes two modified Kuramoto models. The first model integrates with the SIR epidemic model, which accounts for the user states (infected, healthy, or recovered) and simulates the process of information dissemination. The second model incorporates the analysis of user influence by integrating a node centrality coefficient, allowing the identification of key participants who exert the greatest impact on information flows in the network. The proposed models offer several advantages. The SIR-based model enables the evaluation of content dissemination dynamics, localization of disinformation sources, and prediction of its propagation scenarios. The model based on influential users facilitates the identification of key network nodes, which can act as either sources of disinformation or instruments for its containment through the dissemination of verified information. The study highlights the importance of the adaptability of the proposed models to various social platforms and types of information flows. By integrating phase synchronization and node centrality, these models allow for the identification of not only popular users but also those who actively influence informational processes. This provides the ability to respond quickly to informational threats, reducing their impact on society.