Dencik, Lina; Hintz, Arne and Carey, Zoe. 2018. Prediction, pre-emption and limits to dissent: social media and big data uses for policing protests in the United Kingdom. New Media & Society, 20(4), pp. 1433-1450. ISSN 1461-4448 [Article]

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Abstract or Description

Social media and big data uses form part of a broader shift from 'reactive' to 'proactive' forms of governance in which state bodies engage in analysis to predict, pre-empt and respond in real time to a range of social problems. Drawing on research with British police, we contextualize these algorithmic processes within actual police practices, focusing on protest policing. Although aspects of algorithmic decision-making have become prominent in police practice, our research shows that they are embedded within a continuous human-computer negotiation that incorporates a rooted claim to 'professional judgement', an integrated intelligence context and a significant level of discretion. This context, we argue, transforms conceptions of threats. We focus particularly on three challenges: the inclusion of pre-existing biases and agendas, the prominence of marketing-driven software, and the interpretation of unpredictability. Such a contextualized analysis of data uses provides important insights for the shifting terrain of possibilities for dissent.

Item Type:	Article
Identification Number (DOI):	https://doi.org/10.1177/1461444817697722
Additional Information:	Funding: The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: The research for this article was made possible through a grant from the Media Democracy Fund, the Ford Foundation and the Open Society Foundations.
Keywords:	Big data, dissent, predictive policing, protest, social media
Departments, Centres and Research Units:	Media, Communications and Cultural Studies
Dates:	Date Event 13 February 2017 Accepted 20 March 2017 Published Online April 2018 Published
Item ID:	37299
Date Deposited:	17 Jul 2024 11:17
Last Modified:	17 Jul 2024 12:41
Peer Reviewed:	Yes, this version has been peer-reviewed.
URI:	https://research.gold.ac.uk/id/eprint/37299

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