Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Us and them: identifying cyber hate on Twitter across multiple protected characteristics

Burnap, Peter and Williams, Matthew Leighton 2016. Us and them: identifying cyber hate on Twitter across multiple protected characteristics. EPJ Data Science 5 , 11. 10.1140/epjds/s13688-016-0072-6

[img]
Preview
PDF - Published Version
Available under License Creative Commons Attribution.

Download (1MB) | Preview

Abstract

Hateful and antagonistic content published and propagated via the World Wide Web has the potential to cause harm and suffering on an individual basis, and lead to social tension and disorder beyond cyber space. Despite new legislation aimed at prosecuting those who misuse new forms of communication to post threatening, harassing, or grossly offensive language - or cyber hate - and the fact large social media companies have committed to protecting their users from harm, it goes largely unpunished due to difficulties in policing online public spaces. To support the automatic detection of cyber hate online, specifically on Twitter, we build multiple individual models to classify cyber hate for a range of protected characteristics including race, disability and sexual orientation. We use text parsing to extract typed dependencies, which represent syntactic and grammatical relationships between words, and are shown to capture ‘othering’ language - consistently improving machine classification for different types of cyber hate beyond the use of a Bag of Words and known hateful terms. Furthermore, we build a data-driven blended model of cyber hate to improve classification where more than one protected characteristic may be attacked ( e.g. race and sexual orientation), contributing to the nascent study of intersectionality in hate crime.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Social Sciences (Includes Criminology and Education)
Subjects: H Social Sciences > HM Sociology
Q Science > QA Mathematics > QA76 Computer software
Uncontrolled Keywords: cyber hate; hate speech; Twitter; NLP; machine learning
Publisher: SpringerOpen
ISSN: 2193-1127
Funders: Economic and Social Research Council
Last Modified: 04 Jun 2017 08:56
URI: http://orca.cf.ac.uk/id/eprint/88072

Citation Data

Cited 10 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item

Full Text Downloads from ORCA for this publication

Top Downloads of this item by Country

Monthly Full Text Downloads of this item

More statistics for this item...