网络欺凌检测综述

A Survey of Cyberbullying Detection

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.

Cyberbullying has attracted the increasing attention among researcher s.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.