Data Journalism Handbook 1.0 BETA
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The Guardian Datablog’s Coverage of the UK Riots

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Figure 42. The UK Riots: every verified incident (The Guardian)

During the summer of 2011 the UK was hit by a wave of riots. At the time, politicians suggested that these actions were categorically not linked to poverty and those that who did the looting were simply criminals. Moreover, the Prime Minister along with leading conservative politicians blamed social media for causing the riots, suggesting that incitement had taken place on these platforms and that riots were organised using Facebook, Twitter and Blackberry Messenger (BBM). There were calls to temporarily shut social media down. Because the government did not launch an inquiry into why the riots happened, The Guardian Newspaper, in collaboration with the London School of Economics, set up the groundbreaking Reading the Riots project to address these issues.

The newspaper extensively used data journalism to enable the public to better understand who was doing the looting and why. What is more, they also worked with another team of academics, led by Professor Rob Procter at the University of Manchester, to better understand the role of social media, which The Guardian itself had extensively used in its reporting during the riots. The Reading the Riots team is led by Paul Lewis, The Guardian’s Special Projects Editor. During the riots Paul reported on the front-line in cities across England (most notably via his Twitter account, @paullewis). This second team worked on 2.6 million riot tweets donated by Twitter. The main aim of this social media work was to see how rumors circulate on Twitter, the function different users/actors have in propagating and spreading information flows, to see whether the platform was used to incite, and to examine other forms of organization.

In terms of the use of data journalism and data visualizations, it is useful to distinguish between two key periods: the period of the riots themselves and the ways in which data helped tell stories as the riots unfolded; and then a second period of much more intense research with two sets of academic teams working with The Guardian, to collect data, analyze it and write in depth reports on the findings. The results from the first phase of the Reading the Riots project were published during a week of extensive coverage in early December 2011. Below are some key examples of how data journalism was used during both periods.

Phase One: The Riots As They Happened

By using simple maps the Guardian data team showed the locations of confirmed riots spots and through mashing up deprivation data with where the riots took place started debunking the main political narrative that there was no link to poverty. Both of these examples used off the shelf mapping tools and in the second example combine location data with another data set to start making other connections and links.

In relation to the use of social media during the riots, in this case Twitter, the newspaper created a visualization of riot related hashtags used during this period, which highlighted that Twitter was mainly used to respond to the riots rather than to organize people to go looting, with #riotcleanup, the spontaneous campaign to clean up the streets after the rioting, showing the most significant spike during the riot period.

Phase Two: Reading the Riots

When the paper reported its findings from months of intensive research and working closely with two academic teams, two visualizations stand out and have been widely discussed. The first one, a short video, shows the results of combining the known places where people rioted with their home address and showing a so-called ‘riot commute’. Here the paper worked with transport mapping specialist, ITO World, to model the most likely route traveled by the rioters as they made their way to various locations to go looting, highlighting different patterns for different cities, with some traveling long distances.

The second one deals with the ways in which rumors spread on Twitter. In discussion with the academic team, seven rumors were agreed on for analysis. The academic team then collected all data related to each rumor and devised a coding schedule that coded the tweet according to four main codes: people simply repeating the rumor (making a claim), rejecting it (making a counter claim), questioning it (query) or simply commenting (comment). All tweets were coded in triplicate and the results were visualized by the Guardian Interactive Team. The Guardian team has written about how they built the visualization.

What is so striking about this visualization is that it powerfully shows what is very difficult to describe and that is the viral nature of rumors and the ways in which their life cycle plays out over time. The role of the mainstream media is evident in some of these rumors (for example outright debunking them, or indeed confirming them quickly as news), as is the corrective nature of Twitter itself in terms of dealing with such rumors. This visualization not only greatly aided the story telling, but also gave a real insight into how rumors work on Twitter, which provides useful information for dealing with future events.

What is clear from the last example is the powerful synergy between the newspaper and an academic team capable of an in depth analysis of 2.6 million riot tweets. Although the academic team built a set of bespoke tools to do their analysis, they are now working to make these widely available to anyone who wishes to use them in due course, providing a workbench for their analysis. Combined with the how-to description provided by the Guardian team, it will provide a useful case study of how such social media analysis and visualization can be used by others to tell such important stories.

Farida Vis, University of Leicester