Hate in the Machine: Anti-Black and Anti-Muslim Social Media Posts as Predictors of Offline Racially and Religiously Aggravated Crime

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 ‘trig...

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Detalles Bibliográficos
Autor principal: Williams, Matthew L. (Autor)
Otros Autores: Burnap, Pete ; Javed, Amir
Tipo de documento: Electrónico Artículo
Lenguaje:Inglés
Publicado: 2020
En: The British journal of criminology
Acceso en línea: Presumably Free Access
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Palabras clave:
Big data
Far right
Hate crime
Hate speech
Predictive policing
Social media
Descripción
Sumario: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:1464-3529
DOI:10.1093/bjc/azz049