Prediction, pre-emption and limits to dissent: Social media and big data uses for policing protests in the United Kingdom
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 algorit...
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| Published in: | New media & society Vol. 20; no. 4; pp. 1433 - 1450 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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SAGE Publications
01.04.2018
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| ISSN: | 1461-4448, 1461-7315 |
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| 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. |
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| AbstractList | 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. |
| Author | Carey, Zoe Dencik, Lina Hintz, Arne |
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| Cites_doi | 10.1080/1369118X.2012.678878 10.1111/j.1755-618X.2012.01302.x 10.1080/1369118X.2016.1154087 10.4159/harvard.9780674736061 10.1215/9780822375197 10.7238/idp.v0i17.1977 10.1515/9781503625365-007 10.1177/1367549415577396 10.1111/j.1751-9020.2011.00394.x 10.1002/asi.23212 10.1080/10439460500168592 10.24908/ss.v13i3/4.5371 10.7551/mitpress/9042.003.0013 10.2307/1144255 10.1080/10439463.2013.780223 10.1080/13645579.2013.774172 10.1198/jasa.2011.ap09546 |
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| Keywords | dissent Big data protest social media predictive policing |
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| References | Lubbers 2015; 13 Berry 2011; 12 Kitchin 2017; 20 Frické 2014; 66 Aradou, Blanke 2015; 2 boyd, Crawford 2012; 15 Trottier 2012; 49 Mohler, Short, Brantingham 2011; 106 Procter, Crump, Karstedt 2013; 23 Kelling, Bratton 1998; 88 Vitale 2006; 15 Gillham 2011; 6 Procter, Vis, Voss 2013; 16 Trottier 2015; 18 bibr39-1461444817697722 Aradou C (bibr4-1461444817697722) 2015; 2 bibr9-1461444817697722 bibr26-1461444817697722 bibr42-1461444817697722 bibr13-1461444817697722 bibr18-1461444817697722 bibr20-1461444817697722 bibr35-1461444817697722 bibr5-1461444817697722 Elmer G (bibr12-1461444817697722) 2015 bibr14-1461444817697722 Hey T (bibr19-1461444817697722) 2009 bibr27-1461444817697722 bibr41-1461444817697722 Amoore L (bibr1-1461444817697722) 2016 bibr32-1461444817697722 bibr45-1461444817697722 bibr36-1461444817697722 Berry D (bibr7-1461444817697722) 2011; 12 Elmer G (bibr11-1461444817697722) 2008 bibr10-1461444817697722 bibr15-1461444817697722 bibr23-1461444817697722 bibr6-1461444817697722 bibr40-1461444817697722 bibr28-1461444817697722 bibr2-1461444817697722 bibr44-1461444817697722 bibr37-1461444817697722 bibr24-1461444817697722 bibr22-1461444817697722 bibr16-1461444817697722 Manovitch L (bibr31-1461444817697722) 2011 bibr30-1461444817697722 bibr29-1461444817697722 bibr43-1461444817697722 bibr8-1461444817697722 bibr25-1461444817697722 Mayer-Schönberger V (bibr33-1461444817697722) 2013 bibr38-1461444817697722 bibr3-1461444817697722 bibr17-1461444817697722 bibr21-1461444817697722 bibr34-1461444817697722 |
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