European Journal of Cultural Studies

Over the last 30 years or so, human beings have been delegating the work of culture – the sorting, classifying and hierarchizing of people, places, objects and ideas – increasingly to computational processes. Such a shift significantly alters how the category culture has long been practiced, experienced and understood, giving rise to what, following Alexander Galloway, I am calling ‘algorithmic culture’. The purpose of this essay is to trace some of the conceptual conditions out of which algorithmic culture has emerged and, in doing so, to offer a preliminary treatment on what it is. In the vein of Raymond Williams’ Keywords, I single out three terms whose bearing on the meaning of the word culture seems to have been unusually strong during the period in question: information, crowd and algorithm. My claim is that the offloading of cultural work onto computers, databases and other types of digital technologies has prompted a reshuffling of some of the words most closely associated with culture, giving rise to new senses of the term that may be experientially available but have yet to be well named, documented or recorded. This essay, though largely historical, concludes by connecting the dots critically to the present day. What is at stake in algorithmic culture is the gradual abandonment of culture’s publicness and the emergence of a strange new breed of elite culture purporting to be its opposite.

This article is a partial genealogy of the data scientist, meant as a contribution to understanding how both big data and the subject who mines it have come to be. It adds to the growing criticism of data mining by considering how big data might be used to manage the very workers who ostensibly command it. The article traces the concept of ‘sharing’ as it appears in discourses about the knowledge economy, arguing that knowledge sharing produces messy excesses of data. It then traces what is not shared: the knowledge workers capable of mining that data to produce value. It concludes by tracing how the act of sharing knowledge is used to undermine the power of the very subject called forth to command the excesses of sharing. It concludes by describing a reversal: data will become scarce while the ability to mine it ubiquitous and cheap.

Retail, media, finance, science, industry, security and government increasingly depend on predictions produced through techniques such as machine learning. How is it that machine learning can promise to predict with great specificity what differences matter or what people want in many different settings? We need, I suggest, an account of its generalization if we are to understand the contemporary production of prediction. This article maps the principal forms of material action, narrative and problematization that run across algorithmic modelling techniques such as logistic regression, decision trees and Naive Bayes classifiers. It highlights several interlinked modes of generalization that engender increasingly vast data infrastructures and platforms, and intensified mathematical and statistical treatments of differences. Such an account also points to some key sites of instability or problematization inherent to the process of generalization. If movement through data is becoming a principal intersection of power relations, economic value and valid knowledge, an account of the production of prediction might also help us begin to ask how its generalization potentially gives rise to new forms of agency, experience or individuations.

Automated recommendation systems now occupy a central position in the circulation of media and cultural products. Using music as a test case, this article examines the use of algorithms and data mining techniques for the presentation and representation of culture, and how these tools reconfigure the process of cultural intermediation. Expanding Bourdieu’s notion of cultural intermediaries to include technologies like algorithms, I argue that an emerging layer of companies – call them infomediaries – are increasingly responsible for shaping how audiences encounter and experience cultural content. Through a critical analysis of The Echo Nest, a music infomediary whose databases underpin many digital music services, I trace the shift from intermediation to infomediation and explore what is at stake at the intersection of data mining, taste making and audience manufacture. The new infomediary logics at work are computational forms of power that shape popular culture and highlight the social implications of curation by code.

This article examines corporate struggles to reorganize retail environments around the data capturing and processing affordances of digital media. We argue that ongoing transformations in digital retailing reflect and extend the rise of social discrimination around what might be called ‘the quantified individual’. By quantified individual, we mean the hyperfocus on the qualities of the individual person rather than on even the communities or segments relating to people. Drawing on the writings of Charles Taylor, Antonio Gramsci, and Peter Berger and Thomas Luckmann, we use the ongoing corporate refashioning of the general meaning of ‘loyalty’ via the discourses and technologies of retailing as an important example of how a new social imaginary takes form and instantiates social discrimination as normal. For consumers, mobile apps and social-media profiles become venues for performing loyalty and accumulating rewards. For retailers and marketers, digitalized storefronts become like factories for generating data about where individuals go, what they buy and how firms define them. The process is transforming the architecture of physical and digital retailing, and the relationship between the two, in ways that make the selling environment increasingly dynamic and mutable for the individual prospect. We argue that shorn from their 20th century role in the democratization of pricing, stores will become centers of discrimination-related stress as dueling shopper and retailer technologies reach sometimes diverging conclusions about how to encourage loyalty, whom to reward for loyalty, and how.

The recent proliferation of wearable self-tracking devices intended to regulate and measure the body has brought contingent questions of controlling, accessing and interpreting personal data. Given a socio-technical context in which individuals are no longer the most authoritative source on data about themselves, wearable self-tracking technologies reflect the simultaneous commodification and knowledge-making that occurs between data and bodies. In this article, we look specifically at wearable, self-tracking devices in order to set up an analytical comparison with a key historical predecessor, the weight scale. By taking two distinct cases of self-tracking – wearables and the weight scale – we can situate current discourses of big data within a historical framing of self-measurement and human subjectivity. While the advertising promises of both the weight scale and the wearable device emphasize self-knowledge and control through external measurement, the use of wearable data by multiple agents and institutions results in a lack of control over data by the user. In the production of self-knowledge, the wearable device is also making the user known to others, in a range of ways that can be both skewed and inaccurate. We look at the tensions surrounding these devices for questions of agency, practices of the body, and the use of wearable data by courtrooms and data science to enforce particular kinds of social and individual discipline.

The rise of smart phone use, and its convergence with mapping infrastructures and large search and social media corporations, has led to a commensurate rise in the importance of location. While locations are still defined by fixed longitude/latitude coordinates, they now increasingly ‘acquire dynamic meaning as a consequence of the constantly changing location-based information that is attached to them’ becoming ‘a near universal search string for the world’s data’. As the richness of this geocoded information increases, so the commercial value of this location information also increases. This article examines the growing commercial significance of location data. Informed by recent calls for ‘medium-specific analysis’, we build on earlier work to argue that social media companies actively extract location data for commercial advantage in quite specific ways. By not paying due and careful attention to the specifics of data extraction strategies, political and cultural economic analyses of new media services risk eliding key differences between new media platforms, and their respective software systems, patterns of consumer use, and individual revenue models. In response, we develop a comparative analysis of two platforms – Foursquare and Google – and examine how each extracts and uses geocoded user data. From this comparative exploration of platform specificity, we aim to draw conclusions concerning marketing (economic) surveillance, and how Foursquare’s and Google’s operations work in the service of fostering the securitization of mobility - the process by which the capacity to track and predict mobility and associated patterns of consumption is directly productive of value.

Little attention has been paid to the role of the e-book in the networked global information economy. This gap in the scholarship is all the more relevant because the world’s largest purveyor of e-books, Amazon, is one of a group of large digital media corporations such as Google and Apple, currently vying for dominance of a global information economy defined by access to user data and driven by surveillance of consumer preferences, and has itself been at the forefront of developing such technologies. This article investigates the role of e-books in a global information economy where the commodification of data, and of users’ social lives and labour, plays a central role in a neoliberal digital capitalism that brings together the philosophies of libertarian free-market economics, populist participatory discourse, new systems of data-mining and digital surveillance and new regimes of private ownership and commodification of the social.

This article considers how recent developments in open source intelligence impact the inclusion of social media data in policing, and how these changes are a reflection of technological affordances by platform developers and private third-party companies, as well as police cultural and institutional constraints. It explores the institutional contexts in which social media open source intelligence is trialled in Europe by drawing on interviews with police officials and privacy advocates from 13 European Union member states. Respondents considered their adoption of open source social media monitoring technologies in their jurisdiction, locating this uptake within a context of technological scepticism. While open source intelligence infused with social media data enables an immediate access to social life for investigators, so too do they provoke a preoccupation with the origins and circulations of this content. This tension dovetails with institutional concerns, including a lack of specialised staff, budget constraints, along with a lack of clear legal and procedural protocols.

Big Data promises informational abundance – something that might be useful to cultures and communities in times of austerity. However many local organizations lack the skills to develop expertise in new forms of computation or the desire to develop them; Big Data is often viewed as the terrain of Big Business and Big Government. Drawing on issues arising from action research into Big Data and community in Brighton, England, this article explores questions of technological expertise in relation to Big Data, everyday life and critical practice – the latter understood as something that may be undertaken not only as a theoretical but also as an operational endeavour. The outcome of the article is thus not a prescription for training but a series of questions concerning desirable forms of co-constitution: How should expertise be shared between humans and machines?

In this article, I argue that pervasive tracking and data-mining are leading to shifts in governmentality that can be characterised as algorithmic states of exception. I also argue that the apparatus that performs this change owes as much to everyday business models as it does to mass surveillance. I look at technical changes at the level of data structures, such as the move to NoSQL databases, and how this combines with data-mining and machine learning to accelerate the use of prediction as a form of governance. The consequent confusion between correlation and causation leads, I assert, to the creation of states of exception. I set out what I mean by states of exception using the ideas of Giorgio Agamben, focusing on the aspects most relevant to algorithmic regulation: force-of and topology. I argue that the effects of these states of exception escape legal constraints such as concepts of privacy. Having characterised this as a potentially totalising change and an erosion of civil liberties, I ask in what ways the states of exception might be opposed. I follow Agamben by drawing on Walter Benjamin’s concept of pure means as a tactic that is itself outside the frame of law-producing or law-preserving activity. However, the urgent need to respond requires more than a philosophical stance, and I examine two examples of historical resistance that satisfy Benjamin’s criteria. For each, in turn, I draw connections to contemporary cases of digital dissent that exhibit some of the same characteristics. I conclude that it is possible both theoretically and practically to resist the coming states of exception, and I end by warning what is at stake if we do not.

In this article, we investigate the macro-role being played – and played out – by digital, social and ‘new’ media today. We suggest that these media, facilitated by the Internet, can together be understood as a vast simulation machine that mediates and modulates everyday life to refashion what was once the ‘real world’ in its own image. Life in the ‘meatspace’ (the physical world) is most valuable, we suggest, not because it involves tweets, opinions or our desires but because these data produce useful and computable digital resources for finance, business and government. Today’s Big Data mining and predictive analytics allow for digital priorities to become non-digital realities, resulting – we suggest – in the algorithmically generated landscapes of today (and tomorrow). The imperatives driving today’s Internet and mobile technology have more to do with making the world computationally comprehensible than with the facilitation of free expression, open markets or open communication. We discuss the conditions created by these digital simulation machines as well as emerging opportunities for subversion and resistance.