Williams, M. L., Burnap, P., Javed, A., Liu, H., & Ozalp, S. (2019). Hate in the machine: Anti-Black and Anti-Muslim social media posts as predictors of offline racially and religiously aggravated crime. The British Journal of Criminology, azz049.

Link: https://doi.org/10.1093/bjc/azz049

Open access: Yes

Notes: This quantitative study explores the possible correlation between offline and online hate speech—especially around race and religious violence. Overall, they indeed found an association between online and offline offences—which highlights the importance of taking an ecological approach toward violence. However, it is important to note that the author does not offer a direct causality between online hate and offline attacks. They argue, instead, that “It is more likely the case that social media is only part of the formula, and that local level factors, such as the demographic make-up of neighbourhoods (e.g. black and minority ethnic population proportion, unemployment) and other ecological level factors play key roles, as they always have in estimating hate crime.” (p. 112)

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.