Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Prediction, preemption and limits to dissent: social media and big data uses for policing protests in the UK

Dencik, Lina, Hintz, Arne and Carey, Zoe 2017. Prediction, preemption and limits to dissent: social media and big data uses for policing protests in the UK. New Media & Society 20 (4) , pp. 1433-1450. 10.1177/1461444817697722

[img]
Preview
PDF - Published Version
Available under License Creative Commons Attribution.

Download (446kB) | Preview

Abstract

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
Date Type: Published Online
Status: Published
Schools: Journalism, Media and Culture
Subjects: H Social Sciences > H Social Sciences (General)
H Social Sciences > HM Sociology
Uncontrolled Keywords: Big data, dissent, predictive policing, protest, social media
Publisher: SAGE Publications
ISSN: 1461-7315
Funders: Media Democracy Fund, Ford Foundation, Open Society Foundations
Date of First Compliant Deposit: 31 May 2017
Date of Acceptance: 13 February 2017
Last Modified: 26 May 2019 22:09
URI: http://orca.cf.ac.uk/id/eprint/99751

Citation Data

Cited 6 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics