A Survey of Cyberbullying Detection

SONG Yu-qi; GAO Min; LI Jun-dong; RONG Wen-ge; XIONG Qing-yu

doi:10.3969/j.issn.0372-2112.2020.06.025

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A Survey of Cyberbullying Detection

更新时间：2025-07-16

- A Survey of Cyberbullying Detection
- Acta Electronica Sinica Vol. 48, Issue 6, Pages: 1220-1229(2020)
- 作者机构：
  
  1. 弗吉尼亚大学电子与计算机工程学院, 夏洛茨维尔,22904
  2. 信息物理社会可信服务计算教育部重点实验室,重庆,400044
  3. 重庆大学大数据与软件学院,重庆,400044
  4. 弗吉尼亚大学电子与计算机工程学院夏洛茨维尔,22904
  5. 北京航空航天大学计算机学院,北京,100191
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.71102065);Funds for the Central Universities (No.2019CDXYRJ0011);National Key Research and Development Program of China (No.2018YFF0214706);Guangxi Major Science and Technology Project (No.GKAA17129002)
- DOI：10.3969/j.issn.0372-2112.2020.06.025
  CLC： TP391
- Published Online：25 June 2020，
  
  Published：2020
- 稿件说明：
移动端阅览
SONG Yu-qi, GAO Min, LI Jun-dong, et al. A Survey of Cyberbullying Detection[J]. Acta Electronica Sinica, 2020, 48(6): 1220-1229.
DOI：

SONG Yu-qi, GAO Min, LI Jun-dong, et al. A Survey of Cyberbullying Detection[J]. Acta Electronica Sinica, 2020, 48(6): 1220-1229. DOI： 10.3969/j.issn.0372-2112.2020.06.025.

摘要

网络欺凌在社交媒体平台的日益泛滥引起了研究者的广泛关注，社会科学和计算机科学研究者从不同的角度对该问题进行了研究与探讨.为梳理这些研究，本论文对社会科学领域和计算机领域在网络欺凌方面的研究进行了调查分析.首先概述了网络欺凌的基本研究内容和网络欺凌特征，重点讨论了各种用于网络欺凌检测的机器学习方法，包括基于监督学习、基于弱监督学习、基于预设规则和深度学习算法，随后总结了12个现有的网络欺凌检测数据集和常用的检测性能评价指标，最后对基于异构信息网络、融合多种辅助信息和结合心理学特征的欺凌检测方法等进行了展望.

Abstract

Cyberbullying has attracted the increasing attention among researchers. Social and computer science researchers have explored cyberbullying from various perspectives. This paper surveys the existing work on cyberbullying detection in social and computer science domains. It first introduces the basic research problems and characteristics of cyberbullying; second

it discusses a variety of machine learning algorithms for cyberbullying detection

including supervised learning

weakly supervised learning

rule-based and deep learning algorithms; and third

it summarizes 12 existing datasets used in cyberbullying detection and the popular metrics for detection performance. Finally

the paper analyzes the potential research from several aspects

such as cyberbullying detection approaches based on heterogeneous information network

auxiliary information fusion

and psychological characteristics.

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