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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Veröffentlicht in:British journal of criminology Jg. 60; H. 1; S. 93 - 117
Hauptverfasser: Williams, Matthew L, Burnap, Pete, Javed, Amir, Liu, Han, Ozalp, Sefa
Format: Journal Article
Sprache:Englisch
Veröffentlicht: UK Oxford University Press 01.01.2020
Schlagworte:
hate crime
hate speech
big data
far right
social media
predictive policing
ISSN:0007-0955, 1464-3529
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Zusammenfassung: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