Hate speech detection: Challenges and solutions

被引：246

作者：

MacAvaney, Sean ^{[1
]}

Yao, Hao-Ren ^{[1
]}

Yang, Eugene ^{[1
]}

Russell, Katina ^{[1
]}

Goharian, Nazli ^{[1
]}

Frieder, Ophir ^{[1
]}

机构：

[1] Georgetown Univ, Informat Retrieval Lab, Washington, DC 20057 USA

来源：

PLOS ONE | 2019年 / 14卷 / 08期

关键词：

D O I：

10.1371/journal.pone.0221152

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

As online content continues to grow, so does the spread of hate speech. We identify and examine challenges faced by online automatic approaches for hate speech detection in text. Among these difficulties are subtleties in language, differing definitions on what constitutes hate speech, and limitations of data availability for training and testing of these systems. Furthermore, many recent approaches suffer from an interpretability problem-that is, it can be difficult to understand why the systems make the decisions that they do. We propose a multi-view SVM approach that achieves near state-of-the-art performance, while being simpler and producing more easily interpretable decisions than neural methods. We also discuss both technical and practical challenges that remain for this task.

引用

页数：16