Jonathan Bright

The Life and Death of Political News: Measuring the Impact of the Audience Agenda Using Online Data

The rapid development of online media as a major location for news consumption has stimulated a variety of debates about how journalism is changing in the Internet era. Of particular importance have been worries about a potential turn toward populism, whereby journalists and editors shift away from reporting what is newsworthy to what their audience wants to hear supported by the widespread availability of audience metrics. A wealth of ethnographic research has pointed to the potential importance of such statistics; but little quantitative work has been conducted to test for the existence of a relationship between audience behavior and editorial decisions. This study seeks to fill that gap. Based on a novel data set of over 40,000 articles published in five major UK news outlets over a period of 6 weeks, we explore the relationship between a news story’s readership and its likelihood of being removed from the front page, based on the “most read” lists common to many news websites. We find that being a most read article decreased the short-term likelihood of being removed from the front page by around 25% and that this effect was broadly similar for both political and entertainment news. Surprisingly, we find a considerably greater influence in “quality” publications than their tabloid counterparts. Our results are discussed as evidence of a still limited, but potentially developing, turn toward online populism.

Can electoral popularity be predicted using socially generated big data

Abstract Today, our more-than-ever digital lives leave significant footprints in cyberspace. Large scale collections of these socially generated footprints, often known as big data, could help us to re-investigate different aspects of our social collective behaviour in a quantitative framework. In this contribution we discuss one such possibility: the monitoring and predicting of popularity dynamics of candidates and parties through the analysis of socially generated data on the web during electoral campaigns. Such data offer considerable possibility for improving our awareness of popularity dynamics. However they also suffer from significant drawbacks in terms of representativeness and generalisability. In this paper we discuss potential ways around such problems, suggesting the nature of different political systems and contexts might lend differing levels of predictive power to certain types of data source. We offer an initial exploratory test of these ideas, focussing on two data streams, Wikipedia page views and Google search queries. On the basis of this data, we present popularity dynamics from real case examples of recent elections in three different countries.

Securitisation, Terror, and Control: Towards a Theory of the Breaking Point

Securitisations permit the breaking of rules: but which rules? This article argues that any given security situation could be handled by a variety of different ‘rule breaking’ procedures, and that securitisations themselves, whilst permitting rule breaking in general, do not necessarily specify in advance which rules in particular have to be broken. This begs the question: how do specific threats result in specific rule breaking measures? This article explores this question through reference to ‘control orders,’ an unusual legal procedure developed in the UK during the course of the war on terrorism. Once applied to an individual, a control order gives the government a meticulous control over every aspect of their life, up to and including deciding on which educational qualifications they can take. Despite this control, individuals under the regime remain technically ‘free’: and have frequently used this freedom to abscond from the police who are supposed to be watching them. How did a security policy which controls a suspects educational future, but not their physical movements, develop? This article aims to answer this question, and in so doing present a reevaluation of the mechanisms through which the effects of securitisation manifest themselves.

Wikipedia traffic data and electoral prediction: towards theoretically informed models

This aim of this article is to explore the potential use of Wikipedia page view data for predicting electoral results. Responding to previous critiques of work using socially generated data to predict elections, which have argued that these predictions take place without any understanding of the mechanism which enables them, we first develop a theoretical model which highlights why people might seek information online at election time, and how this activity might relate to overall electoral outcomes, focussing especially on information seeking incentives related to swing voters and new parties. We test this model on a novel dataset drawn from a variety of countries in the 2009 and 2014 European Parliament elections. We show that while Wikipedia offers little insight into absolute vote outcomes, it does offer good information about changes in overall turnout at elections and about changes in vote share for particular parties. These results are used to enhance existing theories about the drivers of aggregate patterns in online information seeking, by suggesting that voters are cognitive misers who seek information only when considering changing their vote.

Estimating local commuting patterns from geolocated Twitter data

The emergence of large stores of transactional data generated by increasing use of digital devices presents a huge opportunity for policymakers to improve their knowledge of the local environment and thus make more informed and better decisions. A research frontier is hence emerging which involves exploring the type of measures that can be drawn from data stores such as mobile phone logs, Internet searches and contributions to social media platforms and the extent to which these measures are accurate reflections of the wider population. This paper contributes to this research frontier, by exploring the extent to which local commuting patterns can be estimated from data drawn from Twitter. It makes three contributions in particular. First, it shows that heuristics applied to geolocated Twitter data offer a good proxy for local commuting patterns; one which outperforms the current best method for estimating these patterns (the radiation model). This finding is of particular significance because we make use of relatively coarse geolocation data (at the city level) and use simple heuristics based on frequency counts. Second, it investigates sources of error in the proxy measure, showing that the model performs better on short trips with higher volumes of commuters; it also looks at demographic biases but finds that, surprisingly, measurements are not significantly affected by the fact that the demographic makeup of Twitter users differs significantly from the population as a whole. Finally, it looks at potential ways of going beyond simple frequency heuristics by incorporating temporal information into models.

The real component of virtual learning: motivations for face-to-face MOOC meetings in developing and industrialised countries

Massive open online courses (MOOCs) offer the possibility of entirely virtual learning environments, with lectures, discussions, and assignments all distributed via the internet. The virtual nature of MOOCs presents considerable advantages to students in terms of flexibility to learn what they want, when they want. Yet despite their virtual focus, some MOOC users also seek to create face-to-face communities with students taking similar courses or using similar platforms. This paper aims to assess the learner motivations behind creation of these offline communities. Do these face-to-face meetings represent an added extra to the learning experience, with students taking advantage of the context of the MOOCs to create new personal and professional connections? Or, are offline meetups filling a gap for students who feel that not all learning can take place online? We also assess the extent to which these patterns vary between developing and industrialised regions, thus testing the claim that MOOCs are helping to democratise access to education around the world. Our research is based on a unique source of socially generated big data, drawn from the website ‘meetup.com’, which gives us a data set of over 4000 MOOC related events taking place in over 140 countries around the world over a two year period. We apply a mixed methods approach to this data, combining large-scale analysis with more in-depth thematic hand coding, to more fully explore the reasons why some learners add a ‘real’ component to their virtual learning experience.

Ministerial Importance and Survival in Government. Tough at the Top

Are holders of important ministerial positions more likely to survive in cabinet than their colleagues who hold less important positions? This study examines the relationship between the importance of a ministerial position and the length of time ministers are able to survive in government. It is based on an original dataset of cabinet ministers in seven West European countries from 1945 to 2011. Employing a little-used measure of ministerial survival based on overall time in government, it is found that holders of important ministerial positions are more durable than their colleagues who hold less important ministerial positions. Age, prior government experience and the size of the party to which the minister belongs are also associated with consistently significant effects. Further, the study explores the determinants of survival for two types of risk – exiting government with one’s party and exiting without it – showing that the effects of ministerial importance and other covariates are markedly different for these two types of exit. The findings have important implications for the understanding of ministerial and governmental stability.

Europe’s voting space and the problem of second-order elections: A transnational proposal

This article offers an empirically driven critical consideration of the idea of transnationalising Europe’s voting space, which would mean allowing European citizens to vote for a party from any member state at the European Parliament elections. We argue that such a move would reduce the second-order problem in European elections, as it would force political parties to move away from campaigning solely on national issues. We also claim that it would improve the extent to which Europeans are represented in their parliament and would be particularly welcomed by citizens currently dissatisfied with the state of their national democracy. We offer evidence to back up these claims, based on data on the political preferences of almost half a million Europeans and 274 European parties.

In Search of the Politics of Security

Research Highlights and Abstract This article Provides an exploration of why security politics might change the behaviour of parliaments or legislatures, bringing together theory from diverse fields; Illustrates a series of potential ways of measuring legislative behaviour; Is a systematic quantitative test for the existence of security politics in a field which relies almost exclusively on qualitative methodology; Contributes to the current debate in the field of critical security studies over the definition of the politics of security. This article takes up the recent challenge to critical security studies posed by Browning and McDonald to define the effects of ‘the politics of security’. It focuses in particular on the behaviour of legislatures during the passage of legislation relating to crime and security. Effective scrutiny of this type of policy is crucial, but legislatures are often accused of failing to provide it. However, empirical work in the area remains limited: we know little about exactly how legislatures change their behaviour at such critical junctures. This article seeks to fill this gap. It offers firstly an exploration of diverse strands of work on the notion of ‘security politics’. Secondly, it offers an empirical test based on a dataset covering UK legislation from the period 2007–2012. The results suggest the appearance of security legislation causes parliament to heighten scrutiny, raising questions about the real nature of ‘security politics’.

OpenStreetMap data for alcohol research: reliability assessment and quality indicators

&NA; There is a growing interest in using OpenStreetMap [OSM] data in health research. We evaluate the usefulness of OSM data for researching the spatial availability of alcohol, a field which has been hampered by data access difficulties. We find OSM data is about 50% complete, which appears adequate for replicating findings from other studies using alcohol licensing data. Further, we show how OSM quality metrics can be used to select areas with more complete alcohol data. The ease of access and use may create opportunities for analysts and researchers seeking to understand broad patterns of alcohol availability. HighlightsWe assess the reliability of OpenStreetMap data for estimating spatial availability of alcohol.We show that OSM contains about 50% of actually existing alcohol points of sale.We duplicate a previous study of alcohol related harms using OSM data.We use OSM quality metrics to select areas of higher quality data.We conclude that OSM is a useful supplementary data source for researchers.