Beyond Hostile Linguistic Cues: The Gravity of Online Milieu for Hate Speech Detection in Arabic

被引：4

作者：

Chowdhury, Arijit Ghosh ^{[1
]}

Didolkar, Aniket ^{[1
]}

Sawhney, Ramit ^{[2
]}

Shah, Rajiv Ratn ^{[3
]}

机构：

[1] Manipal Inst Technol, Manipal, Karnataka, India

[2] Netaji Subhas Inst Technol, New Delhi, India

[3] IIIT Delhi, MIDAS, New Delhi, India

来源：

PROCEEDINGS OF THE 30TH ACM CONFERENCE ON HYPERTEXT AND SOCIAL MEDIA (HT '19) | 2019年

关键词：

Arabic-NLP; Social Networks; Hate Speech; Social Media;

D O I：

10.1145/3342220.3344930

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Religious Hate speech poses grave dangers for the cohesion of a democratic society, the protection of human rights and the rule of law. While previous work has shown that linguistic features can be effectively used for text categorization in Arabic, employing information coming from users' social networks has not yet been explored for such complex user characteristics. Systems relying on language information tend to have low precision because they tend to rely on messages containing particular terms indicating hate speech. In this paper, we study the novel problem of exploiting social context for detection of religious hate speech in Arabic tweets, given information extracted from their online milieu by learning a low-dimensional vector representation of users.

引用

页码：285 / 286

页数：2