In the few years since the advent of ‘Big Data’ research, social media analytics has begun to accumulate studies drawing on social media as a resource and tool for research work. Yet, there has been relatively little attention paid to the development of methodologies for handling this kind of data. The few works that exist in this area often reflect upon the implications of ‘grand’ social science methodological concepts for new social media research. By contrast, we advance an abductively oriented (...) methodological suite designed to explore the construction of phenomena played out through social media. To do this, we use a software tool – Chorus – to illustrate a visual analytic approach to data. Informed by visual analytic principles, we posit a two-by-two methodological model of social media analytics, combining two data collection strategies with two analytic modes. We go on to demonstrate each of these four approaches ‘in action’, to help clarify how and why they might be used to address various research questions. (shrink)

Very large databases are a major opportunity for science and data analytics is a remarkable new field of investigation in computer science. The effectiveness of these tools is used to support a “philosophy” against the scientific method as developed throughout history. According to this view, computer-discovered correlations should replace understanding and guide prediction and action. Consequently, there will be no need to give scientific meaning to phenomena, by proposing, say, causal relations, since regularities in very large databases are enough: “with (...) enough data, the numbers speak for themselves”. The “end of science” is proclaimed. Using classical results from ergodic theory, Ramsey theory and algorithmic information theory, we show that this “philosophy” is wrong. For example, we prove that very large databases have to contain arbitrary correlations. These correlations appear only due to the size, not the nature, of data. They can be found in “randomly” generated, large enough databases, which—as we will prove—implies that most correlations are spurious. Too much information tends to behave like very little information. The scientific method can be enriched by computer mining in immense databases, but not replaced by it. (shrink)

Wikipedia is an important instance of “Big Data,” both because it shapes people's frames of reference and because it is a window into the construction—including via crowd-sourcing—of new bodies of knowledge. Based on our own research as well as others' critical and ethnographic work, we take as an instance Wikipedia's evolving representation of the field of sociology and sociologists, including such gendered aspects as male and female scholars and topics associated with masculinity and femininity. Both the gender-specific dynamics surrounding what (...) counts as “notability” on the online encyclopedia and Wikipedia's relative categorical incoherence are discussed. If “Big Data” can be said to construct its own object, it is, in this instance, a curious and lop-sided one, exemplifying pitfalls as well as promise with respect to more accurate and democratic forms of knowledge. (shrink)

In light of recent technological innovations and discourses around data and algorithmic analytics, scholars of many stripes are attempting to develop critical agendas and responses to these developments. In this mutual interview, three scholars discuss the stakes, ideas, responsibilities, and possibilities of critical data studies. The resulting dialog seeks to explore what kinds of critical approaches to these topics, in theory and practice, could open and make available such approaches to a broader audience.

As information systems transform our world, computer scientists design affordances that influence the uses and impacts of these technological objects. This article describes how the practices of design affect the social values materialized in emerging technologies, and explores how design practices can encourage ethical reflection and action. The article presents an ethnography of a laboratory that engineered software for mobile phones to track users’ locations, habits, and behaviors. This technical work raised a number of ethical challenges, particularly around questions of (...) data use and surveillance. The ethnography suggests that particular activities within laboratories can help engineers agree on social values as important to design. It characterizes these activities as values levers: practices that open new conversations about social values and encourage consensus around those values as design criteria. Laboratory leaders and advocates can enable and strengthen these levers to encourage ethical reflection and action as an explicit part of design practice. (shrink)

DNA sequencers, Twitter, MRIs, Facebook, particle accelerators, Google Books, radio telescopes, Tumblr: what do these things have in common? According to the evangelists of “data science,” all of these are instruments for observing reality at unprecedentedly large scales and fine granularities. This perspective ignores the social reality of these very different technological systems, ignoring how they are made, how they work, and what they mean in favor of an exclusive focus on what they generate: Big Data. But no data, big (...) or small, can be interpreted without an understanding of the process that generated them. Statistical data science is applicable to systems that have been designed as scientific instruments, but is likely to lead to confusion when applied to systems that have not. In those cases, a historical inquiry is preferable. (shrink)

Google Trends reveals that at the time we were writing our article on ‘The Coming Crisis of Empirical Sociology’ in 2007 almost nobody was searching the internet for ‘Big Data’. It was only towards the very end of 2010 that the term began to register, just ahead of an explosion of interest from 2011 onwards. In this commentary we take the opportunity to reflect back on the claims we made in that original paper in light of more recent discussions about (...) the social scientific implications of the inundation of digital data. Did our paper, with its emphasis on the emergence of, what we termed, ‘social transactional data’ and ‘digital byproduct data’ prefigure contemporary debates that now form the basis and rationale for this excellent new journal? Or was the paper more concerned with broader methodological, theoretical and political debates that have somehow been lost in all of the loud babble that has come to surround Big Data. Using recent work on the BBC Great British Class Survey as an example this brief paper offers a reflexive and critical reflection on what has become – much to the surprise of its authors – one of the most cited papers in the discipline of sociology in the last decade. (shrink)

Critical Data Studies explore the unique cultural, ethical, and critical challenges posed by Big Data. Rather than treat Big Data as only scientifically empirical and therefore largely neutral phenomena, CDS advocates the view that Big Data should be seen as always-already constituted within wider data assemblages. Assemblages is a concept that helps capture the multitude of ways that already-composed data structures inflect and interact with society, its organization and functioning, and the resulting impact on individuals’ daily lives. CDS questions the (...) many assumptions about Big Data that permeate contemporary literature on information and society by locating instances where Big Data may be naively taken to denote objective and transparent informational entities. In this introduction to the Big Data & Society CDS special theme, we briefly describe CDS work, its orientations, and principles. (shrink)

Recently, there has been renewed interest in so-called evidence-based policy making. Enticed by the grand promises of Big Data, public officials seem increasingly inclined to experiment with more data-driven forms of governance. But while the rise of Big Data and related consequences has been a major issue of concern across different disciplines, attempts to develop a better understanding of the phenomenon's historical foundations have been rare. This short commentary addresses this gap by situating the current push for numerical evidence within (...) a broader socio-political context, demonstrating how the epistemological claims of Big Data science intersect with specific forms of trust, truth, and objectivity. We conclude by arguing that regulators' faith in numbers can be attributed to a distinct political culture, a representative democracy undermined by pervasive public distrust and uncertainty. (shrink)

This article examines how the availability of Big Data, coupled with new data analytics, challenges established epistemologies across the sciences, social sciences and humanities, and assesses the extent to which they are engendering paradigm shifts across multiple disciplines. In particular, it critically explores new forms of empiricism that declare ‘the end of theory’, the creation of data-driven rather than knowledge-driven science, and the development of digital humanities and computational social sciences that propose radically different ways to make sense of culture, (...) history, economy and society. It is argued that: Big Data and new data analytics are disruptive innovations which are reconfiguring in many instances how research is conducted; and there is an urgent need for wider critical reflection within the academy on the epistemological implications of the unfolding data revolution, a task that has barely begun to be tackled despite the rapid changes in research practices presently taking place. After critically reviewing emerging epistemological positions, it is contended that a potentially fruitful approach would be the development of a situated, reflexive and contextually nuanced epistemology. (shrink)

'This book is a masterpiece of theoretical thought. It anticipates the actual achievements of much of what we now call sociolinguistics.