This article considers how Spatial Big Data is situated and produced through embodied spatial experiences as data processes appear and act in small moments on mobile phone applications and other digital spatial technologies. Locating Spatial Big Data in the historical and geographical contexts of Sydney and Hong Kong, it traces how situated knowledges mediate and moderate the rising potency of discourses of cartographic reason and data logics as colonial cartographic imaginations expressed in land divisions and urban planning continue on, in (...) a world that increasingly values models of calculability, interoperability and authority. It draws on ethnographic material gathered through walking interviews in both cities, and in doing so, it argues that by using ethnographic ‘moments’, it is possible to decentre the focus on data processes to consider the critical potential of a politics of everyday experiences that produce and reflect the structures of data logics. Through these ethnographic moments, this article examines how mobile technologies are complicit in the production of Spatial Big Data, and the impact this has on the increasing regimentation and surveillance of modes of being and expression via mobile media. At the same time, it will argue that while spatial calculability has expanded from cartographic reason into data logics, the epistemological universality of Spatial Big Data is constantly being resisted – in moments of experimentation, failure, intuition, memory and desire, the ghosts of the incalculable epistemes, experiences and people, forgotten by the emphasis on calculation, continue to speak. (shrink)

In the age of the digital generation, written public data is ubiquitous and acts as an outlet for today's society. Platforms like Facebook, Twitter, Google+ and LinkedIn have profoundly changed how we communicate and interact. They have enabled the establishment of and participation in digital communities as well as the representation, documentation and exploration of social behaviours, and had a disruptive effect on how we use the Internet. Such digital communications present scholars with a novel way to detect, observe, analyse (...) and understand online communities over time. This article presents the formalization of a Social Observatory: a low latency method for the observation and measurement of social indicators within an online community. Our framework facilitates interdisciplinary research methodologies via tools for data acquisition and analysis in inductive and deductive settings. By focusing our Social Observatory on the public Facebook profiles of 187 federal German politicians we illustrate how we can analyse and measure sentiment, public opinion, and information discourse in advance of the federal elections. To this extent, we analysed 54,665 posts and 231,147 comments, creating a composite index of overall public sentiment and the underlying conceptual discussion themes. Our case study demonstrates the observation of communities at various resolutions: “zooming” in on specific subsets or communities as a whole. The results of the case study illustrate the ability to observe published sentiment and public dialogue as well as the difficulties associated with established methods within the field of sentiment analysis within short informal text. (shrink)

The contribution of Big Data to social science is not limited to data availability but includes the introduction of analytical approaches that have been developed in computer science, and in particular in machine learning. This brings about a new ‘culture’ of statistical modelling that bears considerable potential for the social scientist. This argument is illustrated with a brief discussion of model-based recursive partitioning which can bridge the theory and data-driven approach. Such a method is an example of how this new (...) approach can help revise models that work for the full dataset: it can be used for evaluating different models, a traditional weakness of the ‘traditional’ statistical approach used in social science. (shrink)

This study explores Big Data practices at Facebook through an investigation of the role of commensuration or ‘the transformation of different qualities into a common metric’ in the structuration of analysis and interaction with a major online social media platform. It proposes a conceptual framework and demonstrates the empirical potential of a pragmatic approach based on reading published materials and available documentation. Facebook’s Data Warehousing and Analytics Infrastructure serves as an illustrative example to begin tracing out and describe data assemblages (...) in more detail. In being attentive to the motivations, drivers and challenges engineers face when dealing with Big Data, it is argued that their solutions can enable and support but also constrain specific analytical and transactional capabilities or data flows between various devices and actors. The analysis thus moves beyond methodological critiques of the utility of Big Data that lack empirical support and specificity. It is further argued that analytics not just describe but also actively participate in the enactment of social worlds, thereby opening possibilities for new markets or market segments to arise. Online sociality accounts for a model of the social that makes it visible and measurable qua markets inviting data recontextualisation and the creation of value along multiple axes. Contra Facebook’s claim to make the web more ‘social’, an investigation of commensuration brings to the fore the question how the social is accounted for in the first place. (shrink)

Combining methods in social scientific research has recently gained momentum through a research strand called Mixed Methods Research. This approach, which explicitly aims to offer a framework for combining methods, has rapidly spread through the social and behavioural sciences, and this article offers an analysis of the approach from a field theoretical perspective. After a brief outline of the MMR program, we ask how its recent rise can be understood. We then delve deeper into some of the specific elements that (...) constitute the MMR approach, and we engage critically with the assumptions that underlay this particular conception of using multiple methods. We conclude by offering an alternative view regarding methods and method use. (shrink)

This paper aims to contribute to the development of tools to support an analysis of Big Data as manifestations of social processes and human behaviour. Such a task demands both an understanding of the epistemological challenge posed by the Big Data phenomenon and a critical assessment of the offers and promises coming from the area of Big Data analytics. This paper draws upon the critical social and data scientists’ view on Big Data as an epistemological challenge that stems not only (...) from the sheer volume of digital data but, predominantly, from the proliferation of the narrow-technological and the positivist views on data. Adoption of the social-scientific epistemological stance presupposes that digital data was conceptualised as manifestations of the social. In order to answer the epistemological challenge, social scientists need to extend the repertoire of social scientific theories and conceptual frameworks that may inform the analysis of the social in the age of Big Data. However, an ‘epistemological revolution’ discourse on Big Data may hinder the integration of the social scientific knowledge into the Big Data analytics. (shrink)

Fifteen years ago, Savage and Burrows (2007) warned of a ‘coming crisis’ in empirical sociology. Their article provoked fierce debate within the sociology community – and has subsequently received...

The paper explores the potential and limitations of big data for researching racism on social media. Informed by critical data studies and critical race studies, the paper discusses challenges of doing big data research and the problems of the so called ‘white method’. The paper introduces the following three types of approach, each with a different epistemological basis for researching racism in digital context: 1) using big data analytics to point out the dominant power relations and the dynamics of racist (...) discourse, 2) complementing big data with qualitative research and 3) revealing new logics of racism in datafied context. The paper contributes to critical data and critical race studies by enhancing the understanding of the possibilities and limitations of big data research. This study also highlights the importance of contextualisation and mixed methods for achieving a more nuanced comprehension of racism and discrimination on social media and in large datasets. (shrink)

Working with computational methods and large textual analysis has been challenging and very rewarding—with all the ups and downs that doing empirical social research entails. In my contribution, I relate some research experiences and reflect upon data construction and the links between theory, data, and methods.

How is economics made public? Specifically, how is economics made public on Google? Here we explore a methodological problem – studying google-knowing – and simultaneously explore the more pragmati...

The relationship between politics and the digital has largely been characterized as one of epochal change. The respective theories understand the digital as external to politics and society, as an autonomous driver for global, unilateral transformation. Rather than supporting such singular accounts of the relationship between politics and the digital, this article argues for its specificity: the digital is best examined in terms of folds within existing socio-technical configurations, and as an artefact with a set of affordances that are shaped (...) and filled with meaning by social practice. In conceptualizing the digital as numeric, countable, computable, material, storable, searchable, transferable, networkable and traceable, fabricated and interpreted, it becomes clear that the digital cannot be divorced from the social. These affordances of the digital are discussed in relation to specific political, digital practices that are further developed in the different contributions in this special issue, such as predictive policing (Aradau and Blanke, this issue), data protection (Bellanova, this issue), extremist recruitment videos (Leander, this issue), political acclamation (Dean, this issue), and pandemic simulations (Opitz, this issue). (shrink)

This article maps a controversy in network science over the last 15 years, dividing the field about the epistemic status of a central notion, scale-freeness. The article accounts for the two main disputes, in 2005 and in 2018, as they unfolded in academic publications and on social media. This article analyzes the conflict, and the reasons why it reignited in 2018, to the surprise of many. It is argued that the concept of complex networks is shared by the distinct subcultures (...) of theorists and experimentalists; and that these subcultures have incompatible approaches to knowledge: nomothetic and idiographic. Following Galison, this article contends that network science is a trading zone where theorists and experimentalists can trade knowledge across the epistemic divide. (shrink)

Bold approaches to data collection and large-scale quantitative advances have long been a preoccupation for social science researchers. In this commentary we further debate over the use of large-scale survey data and official statistics with ‘Big Data’ methodologists, and emphasise the ability of these resources to incorporate the essential social and cultural heredity that is intrinsic to the human sciences. In doing so, we introduce a series of new data-sets that integrate approximately 30 years of survey data on victimisation, fear (...) of crime and disorder and social attitudes with indicators of socio-economic conditions and policy outcomes in Britain. The data-sets that we outline below do not conform to typical conceptions of ‘Big Data’. But, we would contend, they are ‘big’ in terms of the volume, variety and complexity of data which has been collated and ‘big’ also in that they allow us to explore key questions pertaining to how social and economic policy change at the national level alters the attitudes and experiences of citizens. Importantly, they are also ‘small’ in the sense that the task of rendering the data usable, linking it and decoding it, required both manual processing and tacit knowledge of the context of the data and intentions of its creators. (shrink)

How a social science of big data would look like? In this article, we exemplify such a social science through a number of cases. We start our discussion with the epistemic qualities of big data. We point out to the fact that contrary to the big data champions, big data is neither new nor a miracle without any error nor reliable and rigorous as assumed by its cheer leaders. Secondly, we identify three types of big data: natural big data, artificial (...) big data and human big data. We present and discuss in what ways they are similar and in what other ways they differ. The assumption of a homogenous big data in fact misleads the relevant discussions. Thirdly, we extended 3 Vs of the big data and add veracity with reference to other researchers and violability which is the current author’s proposal. We explain why the trinity of Vs is insufficient to characterize big data. Instead, a quintinity is proposed. Fourthly, we develop an economic analogy to discuss the notions of data production, data consumption, data colonialism, data activism, data revolution, etc. In this context, undertaking a Marxist approach, we explain what we mean by data fetishism. Fifthly, we reflect on the implications of growing up with big data, offering a new research area which is called as developmental psychology of big data. Finally, we sketch data resistance and the newly proposed notion of omniresistance, i.e. resisting anywhere at any occasion against the big brother watching us anywhere and everywhere. (shrink)

The paper discusses the extent to which the availability of unprecedentedly rich data-sets and the need for new approaches – both epistemological and computational – is an emerging issue for Macroeconomics. By adopting an evolutionary approach, we describe the paradigm shifts experienced in the macroeconomic research field and emphasize that the types of data the macroeconomist has to deal with play an important role in the evolutionary process of the development of the discipline. After introducing the current debate over Big (...) Data in social sciences, the paper presents a detailed discussion of possible and existing interactions between Big Data and Computational Behavioral Macroeconomics. We argue that Big Data applied to economic questions can lead to new styles of thinking and research methods, namely to the development of a new research paradigm. (shrink)

As researchers we often find ourselves grappling with social media platforms and data ‘at close quarters’. Although social media platforms were created for purposes other than academic research – which are apparent in their architecture and temporalities – they offer opportunities for researchers to repurpose them for the collection, generation and analysis of rich datasets. At the same time, this repurposing raises an evolving range of practical and methodological challenges at the small and large scale. We draw on our experiences (...) and empirical data from two research projects, one using Facebook Community Pages and the other repurposing Facebook Activity Logs. This article reflects critically on the specific challenges we faced using these platform features, on their common roots, and the tactics we adopted in response. De Certeau’s distinction between strategy and tactics provides a useful framework for exploring these struggles as located in the practice of doing social research – which often ends up being tactical. This article argues that we have to collectively discuss, demystify and devise tactics to mitigate the strategies and temporalities deeply embedded in platforms, corresponding as far as possible to the temporalities and the aims of our research. Although combat at close quarters is inevitable in social media research, dialogue between researchers is more than ever needed to tip the scales in our favour. (shrink)

In this contribution to the colloquium, I argue why and how I lost interest in the overall structure of social networks even though Big Data techniques are increasingly simplifying the collection, organisation, and analysis of ever larger networks. The challenge that Big Data techniques pose to the social scientist, I think, is of a different nature. Big Data on social actors mainly record events, e.g. interactions between human beings that happen at a point in time. In contrast, social network analysts (...) tend to think in terms of social relations that exist over a timespan. The challenge, then, is to rethink our conceptions and models of social relations and social structure. I conceptualize social structure and social relations as forces. I propose modelling these forces with regression models for longitudinal interaction data. (shrink)

Recent work on Big Data and analytics reveals a tension between analyzing the role of emerging objects and processes in existing systems and using those same objects and processes to create new and purposeful forms of action. While the field of science and technology studies has had considerable success in pursuing the former goal, as Halford and Savage argue, there is an ongoing need to discover or invent ways to “do Big Data analytics differently.” In this commentary, I suggest that (...) attempts to produce new ways of working with Big Data and analytics might be hindered by how science and technology studies-influenced scholars have conceptualized assemblages. While these scholars have foregrounded objects’ relations within existing assemblages, new materialist philosophers draw attention to properties of objects that transcend those relations and might indicate opportunities for more creative or generative uses of Big Data and analytics. (shrink)

The article presents a conceptual framework for distinguishing different sorts of heterogeneous digital materials. The hypothesis is that a wide range of heterogeneous data resources can be characterized and classified due to their particular configurations of hypertext features such as scripts, links, interactive processes, and time scalings, and that the hypertext configuration is a major but not sole source of the messiness of big data. The notion of hypertext will be revalidated, placed at the center of the interpretation of networked (...) digital media, and used in the analysis of the fast-growing amounts of heterogeneous digital collections, assemblages, and corpora. The introduction summarizes the wider background of a fast-changing data landscape. (shrink)