  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.
