Hate in the Machine: Anti-Black and Anti-Muslim Social Media Posts as Predictors of Offline Racially and Religiously Aggravated Crime
Abstract National governments now recognize online hate speech as a pernicious social problem. In the wake of political votes and terror attacks, hate incidents online and offline are known to peak in tandem. This article examines whether an association exists between both forms of hate, independent...
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| Published in: | British journal of criminology Vol. 60; no. 1; pp. 93 - 117 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
UK
Oxford University Press
01.01.2020
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| ISSN: | 0007-0955, 1464-3529 |
| Online Access: | Get full text |
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| Abstract | Abstract
National governments now recognize online hate speech as a pernicious social problem. In the wake of political votes and terror attacks, hate incidents online and offline are known to peak in tandem. This article examines whether an association exists between both forms of hate, independent of ‘trigger’ events. Using Computational Criminology that draws on data science methods, we link police crime, census and Twitter data to establish a temporal and spatial association between online hate speech that targets race and religion, and offline racially and religiously aggravated crimes in London over an eight-month period. The findings renew our understanding of hate crime as a process, rather than as a discrete event, for the digital age. |
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| AbstractList | Abstract
National governments now recognize online hate speech as a pernicious social problem. In the wake of political votes and terror attacks, hate incidents online and offline are known to peak in tandem. This article examines whether an association exists between both forms of hate, independent of ‘trigger’ events. Using Computational Criminology that draws on data science methods, we link police crime, census and Twitter data to establish a temporal and spatial association between online hate speech that targets race and religion, and offline racially and religiously aggravated crimes in London over an eight-month period. The findings renew our understanding of hate crime as a process, rather than as a discrete event, for the digital age. |
| Author | Burnap, Pete Javed, Amir Ozalp, Sefa Liu, Han Williams, Matthew L |
| Author_xml | – sequence: 1 givenname: Matthew L surname: Williams fullname: Williams, Matthew L email: WilliamsM7@cf.ac.uk organization: School of Social Sciences and HateLab, Cardiff University, Cardiff, UK – sequence: 2 givenname: Pete surname: Burnap fullname: Burnap, Pete organization: School of Computer Science and Informatics and HateLab, Cardiff University, Cardiff, UK – sequence: 3 givenname: Amir surname: Javed fullname: Javed, Amir organization: School of Computer Science and Informatics, Cardiff University, Cardiff, UK – sequence: 4 givenname: Han surname: Liu fullname: Liu, Han organization: School of Computer Science and Informatics, Cardiff University, Cardiff, UK – sequence: 5 givenname: Sefa surname: Ozalp fullname: Ozalp, Sefa organization: School Social Sciences, Cardiff University, Cardiff, UK |
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| ContentType | Journal Article |
| Copyright | The Author(s) 2019. Published by Oxford University Press on behalf of the Centre for Crime and Justice Studies (ISTD). 2019 |
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| Keywords | hate crime hate speech big data far right social media predictive policing |
| Language | English |
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National governments now recognize online hate speech as a pernicious social problem. In the wake of political votes and terror attacks, hate... |
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| Title | Hate in the Machine: Anti-Black and Anti-Muslim Social Media Posts as Predictors of Offline Racially and Religiously Aggravated Crime |
| Volume | 60 |
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