Role Identification based Method for Cyberbullying Analysis in Social Edge Computing

Runyu Wang, Tun Lu, Peng Zhang, Ning Gu
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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.
基于角色识别的社交边缘计算网络欺凌分析方法
在过去的几年里,人们致力于研究社会边缘计算设备中的网络欺凌问题,其中大部分都集中在三个角色上:受害者、施暴者和旁观者。如果我们想深入了解互联网边缘设备中网络欺凌的形成、演变和干预,就有必要探索更精细的角色。本文介绍了一种多层次的角色特征建模方法,并提出了一种差分进化辅助 K-均值(DEK)方法来识别多样化的角色。我们的工作旨在为社交边缘计算环境中的网络欺凌场景提供一种角色识别方案,以缓解网络欺凌带来的普遍安全问题。在从微博获取的 10 个真实世界数据集和 5 个公开数据集上进行的实验表明,所提出的 DEK 在方法层面上优于现有方法。经过聚类,我们得到了九个角色,并分析了每个角色在不同网络欺凌场景下的特征及其演变趋势。本文的研究成果可以应用于互联网边缘的设备,从而获得更好的实时识别性能,并适应移动设备广泛的地理位置和高流动性的特点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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