网络欺凌检测综述

doi:10.3969/j.issn.0372-2112.2020.06.025

电子学报 ›› 2020, Vol. 48 ›› Issue (6): 1220-1229.DOI: 10.3969/j.issn.0372-2112.2020.06.025

网络欺凌检测综述

宋宇琦^1,2, 高旻^1,2, 李骏东³, 荣文戈⁴, 熊庆宇^1,2

1. 信息物理社会可信服务计算教育部重点实验室, 重庆 400044;
2. 重庆大学大数据与软件学院, 重庆 400044;
3. 弗吉尼亚大学电子与计算机工程学院, 夏洛茨维尔 22904;
4. 北京航空航天大学计算机学院, 北京 100191

收稿日期:2019-01-02 修回日期:2019-11-21 出版日期:2020-06-25 发布日期:2020-06-25
通讯作者: 高旻
作者简介:宋宇琦 女,1994年生,2019年6月毕业于重庆大学大数据与软件学院,获得硕士学位.主要研究方向为机器学习、虚假用户检测等. E-mail:songyq@cqu.edu.cn
李骏东 男,1990年生,工学博士,美国弗吉尼亚大学电子与计算机工程和计算机科学系助理教授.主要研究兴趣为数据挖掘和机器学习. E-mail:jundong@virginia.edu
荣文戈 男,1975年生,工学博士,北京航空航天大学计算机学院教授、博士生导师.主要研究方向为机器学习、自然语言处理、数据挖掘和信息系统等. E-mail:w.rong@buaa.edu.cn
熊庆宇 男,1965年生,工学博士,重庆大学大数据与软件学院教授、博士生导师.2002年3月毕业于日本九州大学.主要研究方向为智能控制、传感器网络和信息系统. E-mail:xiong03@cqu.edu.cn
基金资助:
国家自然科学基金（No.71102065）；中央高校基金（No.2019CDXYRJ0011）；国家重点研发计划（No.2018YFF0214706）；广西科技重大专项（No.GKAA17129002）

A Survey of Cyberbullying Detection

SONG Yu-qi^1,2, GAO Min^1,2, LI Jun-dong³, RONG Wen-ge⁴, XIONG Qing-yu^1,2

1. Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing 400044, China;
2. School of Big Data&Software Engineering, Chongqing University, Chongqing 400044, China;
3. Department of Electrical and Computer Engineering, University of Virginia, VA 22904, US;
4. School of Computer Science and Engineering, Beihang University, Beijing 100191, China

Received:2019-01-02 Revised:2019-11-21 Online:2020-06-25 Published:2020-06-25

摘要/Abstract

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.

关键词: 网络欺凌, 欺凌检测, 机器学习, 社交网络, 欺凌特征, 网络欺凌数据集

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.

Key words: cyberbullying, bullying detection, machine learning, social network, bullying feature, cyberbullying dataset

中图分类号:

TP391

宋宇琦, 高旻, 李骏东, 荣文戈, 熊庆宇. 网络欺凌检测综述[J]. 电子学报, 2020, 48(6): 1220-1229.

SONG Yu-qi, GAO Min, LI Jun-dong, RONG Wen-ge, XIONG Qing-yu. A Survey of Cyberbullying Detection[J]. Acta Electronica Sinica, 2020, 48(6): 1220-1229.

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网络欺凌检测综述

A Survey of Cyberbullying Detection

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