Role Identification based Method for Cyberbullying Analysis in Social Edge Computing

TL;DR

This paper introduces a multi-level role identification method using differential evolution-assisted K-means clustering to analyze cyberbullying roles in social edge computing, revealing nine distinct roles and their evolution in real-world datasets.

Contribution

It proposes a novel role feature modeling and clustering approach tailored for cyberbullying analysis in social edge devices, enhancing role differentiation and real-time detection capabilities.

Findings

Outperforms existing methods in role identification accuracy

Identifies nine distinct cyberbullying roles from datasets

Analyzes role evolution under different scenarios

Abstract

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.