Data-Driven Agent-Based Model for Public Opinion Propagation Simulation in Cyberbullying - Runyu Wang; Tun Lu; Peng Zhang; Ning Gu (Big Data Mining and Analytics, 2025)
The flourishing development of social media platforms based on cultivating user relationships to spread and share information has provided a breeding ground for cyberbullying. How to infer the evolution of public opinion propagation in an online bullying environment is of great significance to the governance of cyberbullying. In this paper, we propose a data-driven agent-based Model for Public Opinion Propagation Simulation (MPOPS) in cyberbullying. First, we design a Public Opinion Propagation Environment for Opinion Fusion and Polarization (OFP-POPE) in a cyber-physical-social space. Second, we conduct agent-based fine-grained modeling based on the OFP-POPE. Third, we define and quantify user interaction behaviors and improve the Susceptible-Exposed-Infected Removed (SEIR) model by taking these interaction behaviors as the factors influencing public opinion propagation. Finally, we take the “2022 Tangshan restaurant attack” incident as an empirical study case and conduct simulation experiments on the MPOPS driven by real-world data. The experimental results show that the MPOPS is superior to other baseline models, and can simulate the evolutionary trend of public opinion propagation in actual cyberbullying scenarios. In addition, the ablation experiment and parameter sensitivity analysis provide a reference for future cyberbullying intervention efforts.