当前位置: X-MOL 学术New Rev. Hypermedia Multimed. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Characterizing usage of explicit hate expressions in social media
New Review of Hypermedia and Multimedia ( IF 1.2 ) Pub Date : 2018-04-03 , DOI: 10.1080/13614568.2018.1489001
Mainack Mondal 1 , Leandro Araújo Silva 2 , Denzil Correa 3 , Fabrício Benevenuto 2
Affiliation  

ABSTRACT Social media platforms provide an inexpensive communication medium that allows anyone to publish content and anyone interested in the content can obtain it. However, this same potential of social media provide space for discourses that are harmful to certain groups of people. Examples of these discourses include bullying, offensive content, and hate speech. Out of these discourses hate speech is rapidly recognized as a serious problem by authorities of many countries. In this paper, we provide the first of a kind systematic large-scale measurement and analysis study of explicit expressions of hate speech in online social media. We aim to understand the abundance of hate speech in online social media, the most common hate expressions, the effect of anonymity on hate speech, the sensitivity of hate speech and the most hated groups across regions. In order to achieve our objectives, we gather traces from two social media systems: Whisper and Twitter. We then develop and validate a methodology to identify hate speech on both of these systems. Our results identify hate speech forms and unveil a set of important patterns, providing not only a broader understanding of online hate speech, but also offering directions for detection and prevention approaches.

中文翻译:

表征社交媒体中明确的仇恨表达的使用

摘要 社交媒体平台提供了一种廉价的通信媒介,允许任何人发布内容,任何对内容感兴趣的人都可以获取它。然而,社交媒体的同样潜力为对某些人群有害的话语提供了空间。这些话语的例子包括欺凌、攻击性内容和仇恨言论。在这些话语中,仇恨言论很快被许多国家的当局认定为一个严重的问题。在本文中,我们首次对在线社交媒体中仇恨言论的露骨表达进行了系统的大规模测量和分析研究。我们的目标是了解在线社交媒体中大量的仇恨言论、最常见的仇恨表达、匿名对仇恨言论的影响、仇恨言论的敏感性和各地区最受仇恨的群体。为了实现我们的目标,我们从两个社交媒体系统收集痕迹:Whisper 和 Twitter。然后,我们开发并验证了一种方法来识别这两个系统上的仇恨言论。我们的结果确定了仇恨言论的形式并揭示了一组重要的模式,不仅提供了对在线仇恨言论的更广泛理解,而且还提供了检测和预防方法的方向。
更新日期:2018-04-03
down
wechat
bug