Hate in the machine: anti-black and anti-Muslim social media posts as predictors of offline racially and religiously aggravated crime

Williams, Matthew L. ORCID: https://orcid.org/0000-0003-2566-6063, Burnap, Pete ORCID: https://orcid.org/0000-0003-0396-633X, Javed, Amir ORCID: https://orcid.org/0000-0001-9761-0945, Liu, Han ORCID: https://orcid.org/0000-0002-7731-8258 and Ozalp, Sefa ORCID: https://orcid.org/0000-0002-4104-1541 2020. Hate in the machine: anti-black and anti-Muslim social media posts as predictors of offline racially and religiously aggravated crime. British Journal of Criminology 60 (1) , pp. 93-117. 10.1093/bjc/azz049

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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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics Social Sciences (Includes Criminology and Education)
Additional Information:	This is an open access article distributed under the terms of the Creative Commons CC BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher:	Oxford University Press
ISSN:	0007-0955
Funders:	ESRC, US DoJ
Date of First Compliant Deposit:	25 July 2019
Date of Acceptance:	18 July 2019
Last Modified:	24 Mar 2024 15:04
URI:	https://orca.cardiff.ac.uk/id/eprint/124470

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