The emergence of big data offers not only a potential boon for social scientific inquiry, but also raises distinct epistemological issues for this new area of research. Drawing on interviews conducted with researchers at the forefront of big data research, we offer insight into questions of causal versus correlational research, the use of inductive methods, and the utility of theory in the big data age. While our interviewees acknowledge challenges posed by the emergence of big data approaches, they reassert the importance of fundamental tenets of social science research such as establishing causality and drawing on existing theory. They also discussed more pragmatic issues, such as collaboration between researchers from different fields, and the utility of mixed methods. We conclude by putting the themes emerging from our interviews into the broader context of the role of data in social scientific inquiry, and draw lessons about the future role of big data in research.
The following is a slightly edited version of a talk I gave at the Data Power conference in Sheffield this week, presenting work by myself and Ralph Schroeder.
The question of what drives news coverage far pre-dates the Internet and the rise of social media, and over the decades – or indeed the centuries – of mass media, myriad explanations have been offered in answer. Continue reading “Big Data – What’s New(s)?”→
The increasing abundance of data creates new opportunities for communities of interest and communities of practice. We believe that interactive tabletops will allow users to explore data in familiar places such as living rooms, cafés, and public spaces. We propose informal, mobile possibilities for future generations of flexible and portable tabletops. In this paper, we build upon current advances in sensing and in organic user interfaces to propose how tabletops in the future could encourage collaboration and engage users in socially relevant data-oriented activities. Our work focuses on the socio-technical challenges of future democratic deliberation. As part of our vision, we suggest switching from fixed to mobile tabletops and provide two examples of hypothetical interface types: TableTiles and Moldable Displays. We consider how tabletops could foster future civic communities, expanding modes of participation originating in the Greek Agora and in European notions of cafés as locales of political deliberation.
This paper is the product of a workshop that brought together practitioners, researchers, and data experts to discuss how big data is becoming a resource for positive social change in low- and middle-income countries (LMICs). We include in our definition of big data sources such as social media data, mobile phone use records, digitally mediated transactions, online news media sources, and administrative records. We argue that there are four main areas where big data has potential for promoting positive social change: advocacy; analysis and prediction; facilitating information exchange; and promoting accountability and transparency. These areas all have particular challenges and possibilities, but there are also issues shared across them, such as open data and privacy concerns. Big data is shaping up to be one of the key battlefields of our time, and the paper argues that this is therefore an opportune moment for civil society groups in particular to become a larger part of the conversation about the use of big data, since questions about the asymmetries of power involved are especially urgent in these uses in LMICs. Civil society groups are also currently underrepresented in debates about privacy and the rights of technology users, which are dominated by corporations, governments and nongovernmental organizations in the Global North. We conclude by offering some lessons drawn from a number of case studies that represent the current state-of-the-art.
This position paper addresses current debates about data in general, and big data specifically, by examining the ethical issues arising from advances in knowledge production. Typically ethical issues such as privacy and data protection are discussed in the context of regulatory and policy debates. Here we argue that this overlooks a larger picture whereby human autonomy is undermined by the growth of scientific knowledge. To make this argument, we first offer definitions of data and big data, and then examine why the uses of data-driven analyses of human behaviour in particular have recently experienced rapid growth. Next, we distinguish between the contexts in which big data research is used, and argue that this research has quite different implications in the context of scientific as opposed to applied research. We conclude by pointing to the fact that big data analyses are both enabled and constrained by the nature of data sources available. Big data research will nevertheless inevitably become more pervasive, and this will require more awareness on the part of data scientists, policymakers and a wider public about its contexts and often unintended consequences.
One of the early discussions emerging at our ‘Big Data for Social Change’ at the Rockefeller Center in Bellagio surrounds how the act of capturing of big data impinges on our understanding of it. There are three strands in particular which have been flagged up. Firstly, who does the counting? As Marc Ventresca has showed, the shift from ecclesiastical to secular authority in the collection of data affected perceptions of society, for example shifting the focus to the individual from the collective. The national census is not an impassive, aloof process but rather a culturally and politically significant object, reflecting and reinforcing societal debate and conflict. This significance is reflected in the 1918 observation that, “the science of statistics is the chief instrumentality through which the progress of civilization is now measured, and by which its development hereafter will be largely controlled”.Continue reading “Big Data in Bellagio: who counts, what counts, and how do we count?”→
During the construction of a jigsaw or model, there is invariably a moment in which one’s perception shifts from the level of ‘parts’ to the level of ‘whole’ – when, as it were, the bigger picture becomes clear. (Presumably the German language offers an elegant compound noun for this, but I am yet to come across it.) Since its ascension from first appearance to its current perch at the peak of inflated expectations, big data as a phenomenon has seemed to operate primarily on the level of parts or pieces. These usually take the form of noteworthy findings from or utilisations of big data that are eye-opening for one reason or another. Continue reading “Piecing Together the Value of Big Data”→