Policing cyber-neighbourhoods: tension monitoring and social media networks

Abstract

We propose that late modern policing practices, that rely on neighbourhood intelligence, the monitoring of tensions, surveillance and policing by accommodation, need to be augmented in light of emerging ‘cyber-neighbourhoods’, namely social media networks. The 2011 riots in England were the first to evidence the widespread use of social media platforms to organise and respond to disorder. The police were ill-equipped to make use of the intelligence emerging from these non-terrestrial networks and were found to be at a disadvantage to the more tech-savvy rioters and the general public. In this paper, we outline the development of the ‘tension engine’ component of the Cardiff Online Social Media ObServatroy (COSMOS). This engine affords users with the ability to monitor social media data streams for signs of high tension which can be analysed in order to identify deviations from the ‘norm’ (levels of cohesion/low tension). This analysis can be overlaid onto a palimpsest of curated data, such as official statistics about neighbourhood crime, deprivation and demography, to provide a multidimensional picture of the ‘terrestrial’ and ‘cyber’ streets. As a consequence, this ‘neighbourhood informatics’ enables a means of questioning official constructions of civil unrest through reference to the user-generated accounts of social media and their relationship to other, curated, social and economic data.

Keywords:

• social media
• tension monitoring
• public order
• riot
• Cardiff Online Social Media ObServatory

Acknowledgements

This work is based on the project Digital Social Research Tools, Tension Indicators and Safer Communities: a Demonstration of the Cardiff Online Social Media ObServatory (COSMOS) which was funded by the Economic and Social Research Council under the Digital Social Research Demonstrator Programme (Grant Reference: ES/J009903/1). We are grateful for comments from the reviewers on the first draft of this paper.

Notes

1. http://www.cardiff.ac.uk/socsi/research/researchgroups/comsc-socsi/projects.html.

2. http://blog.Twitter.com/2011/09/one-hundred-million-voices.html.

3. Derived from crowd-sourcing: the act of outsourcing a task to a large, undefined group of people through an open call.

4. The Cardiff Online Social Media ObServatory (COSMOS) will be the first platform to integrate social media analytics with secondary data from all these sources. COSMOS is currently a case study for the Office for National Statistics (ONS) Application Programming Interface (API) project, which aims to provide access to all ONS datasets.

5. Beckett's model was adapted by ACPO in 1992: Sporadic disorder → Riot → Serious rioting → Lethal rioting (as the apex) → Immediate post-riot → Community unrest (ACPO 1992) and altered in 2000 to the ‘disorder model’ to reflect a more holistic approach to public order policing: Unrest → Serious disorder/Riot → Disorder → Tension. See King and Waddington (2004) for a discussion of the ‘riot curve’ and its development.

6. https://dev.twitter.com/docs/faq#6861.

7. ‘Where tentative conclusions are deemed acceptable, may an α≥0.667 suffice’ (Krippendorff 2004, p. 241).