CIOG: Coupled dynamics of information spreading and opinion evolution through social media groups

The spread of misinformation through social media groups exacerbates opinion polarization, making it important to understand the influence of information spreading in the opinion evolution in order to mitigate the harmful impact of misinformation. In the real world, the two processes exhibit evolution differences as the state of spreading behavior is step-changed while the state of opinion is changed continuously. However, most existing models use unified discrete or continuous models to simulate the two processes in coupling scenarios, and adopt standard network representations that only consider binary interactions and ignore group interactions. Their neglect of the details leads to deficiencies in modeling change processes on real-world social networks. This paper proposes a Coupled dynamic model of Information spreading and Opinion evolution through social media Groups (CIOG). To quantify the coupling between different dynamics, CIOG constructs an asymmetric coupling incentive function, correlating the discrete information spreading process with the continuous opinion evolution process. CIOG uses hypergraphs to characterize the interaction mechanism among multiple individuals, providing a natural representation for real-world networks. Application experiments and simulation experiments on real datasets validate the effectiveness of CIOG and provide information spreading intervention strategies in coupling scenarios, showing the ability of CIOG to capture complex processes in the real world.