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 finegrained 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 showed that the MPOPS is superior to other baseline models and can simulate the evolutionary trend of public opinion propagation in actual cyberbullying scenarios. Meanwhile, the ablation experiment and sensitivity analysis of the parameters provided the conference with further intervention in cyberbullying.

Over the past few years, many efforts have been dedicated to studying cyberbullying in social edge computing devices, and most of them focus on three roles: victims, perpetrators, and bystanders. If we want to obtain a deep insight into the formation, evolution, and intervention of cyberbullying in devices at the edge of the Internet, it is necessary to explore more fine-grained roles. This paper presents a multi-level method for role feature modeling and proposes a differential evolution-assisted K-means (DEK) method to identify diverse roles. Our work aims to provide a role identification scheme for cyberbullying scenarios for social edge computing environments to alleviate the general safety issues that cyberbullying brings. The experiments on ten real-world datasets obtained from Weibo and five public datasets show that the proposed DEK outperforms the existing approaches on the method level. After clustering, we obtained nine roles and analyzed the characteristics of each role and their evolution trends under different cyberbullying scenarios. Our work in this paper can be placed in devices at the edge of the Internet, leading to better real-time identification performance and adapting to the broad geographic location and high mobility of mobile devices.