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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Bibliographic Details
Published in:British journal of criminology Vol. 60; no. 1; pp. 93 - 117
Main Authors: Williams, Matthew L, Burnap, Pete, Javed, Amir, Liu, Han, Ozalp, Sefa
Format: Journal Article
Language:English
Published: UK Oxford University Press 01.01.2020
Subjects:
hate crime
hate speech
big data
far right
social media
predictive policing
ISSN:0007-0955, 1464-3529
Online Access:Get full text
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Summary: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.
ISSN:0007-0955
1464-3529
DOI:10.1093/bjc/azz049