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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. [ABSTRACT FROM AUTHOR]

The Domestic Abuse, Stalking and Honour Based Violence (DASH) form is a standardized risk assessment implemented across most UK police forces. It is intended to facilitate an officer's structured professional judgment about the risk a victim faces of serious harm at the hand of their abuser. Until now, it has been an open question whether this tool works in practice. Here, we present the largest scale European study, making the case that the risk assessment tool is underperforming. Each element of the DASH questionnaire is, at best, weakly predictive of revictimization. Officer risk predictions based on DASH are little better than random and a logistic regression model that predicts the same outcome using DASH only provides modest improvement in performance. [ABSTRACT FROM AUTHOR]

Patterns are the epistemological core of predictive policing. With the move towards digital prediction tools, the authority of the pattern is rearticulated and reinforced in police work. Based on empirical research about predictive policing software and practices, this article puts the authority of patterns into perspective. Introducing four ideal-typical styles of pattern identification, we illustrate that patterns are not based on a singular logic, but on varying rationalities that give form to and formalize different understandings about crime. Yet, patterns render such different modes of reasoning about crime, and the way in which they feed back into policing cultures, opaque. Ultimately, this invites a stronger reflection about the political nature of patterns. [ABSTRACT FROM AUTHOR]

This paper critically examines the affordances and limitations of big data for the study of crime and disorder. We hypothesize that disorder-related posts on Twitter are associated with actual police crime rates. Our results provide evidence that naturally occurring social media data may provide an alternative information source on the crime problem. This paper adds to the emerging field of computational criminology and big data in four ways: (1) it estimates the utility of social media data to explain variance in offline crime patterns; (2) it provides the first evidence of the estimation offline crime patterns using a measure of broken windows found in the textual content of social media communications; (3) it tests if the bias present in offline perceptions of disorder is present in online communications; and (4) it takes the results of experiments to critically engage with debates on big data and crime prediction. [ABSTRACT FROM AUTHOR]