Issue 2 Crowds and Clouds »

Preface: Crowds and Clouds


Preface: Crowds and Clouds

This issue of LIMN aims to raise the level of discussion about new social media, crowdsourcing, cloud computing, big data, and Internet revolutions. Too often, writing about these things follows well-worn paths of argument—paths that become increasingly worn with every rehearsal. The pieces herein seek to interrupt that path, to cross it at odd angles, to find another way through the complex thicket of technology and society.

Take for example the phenomenon known as “Big Data” and the miraculous new forms of problem-solving, knowledge creation and economic productivity it promises. The buzzwords of the brave new world of big data include “cloud computing,” “algorithms,” “filters,” “virtualization,” and “scalable infrastructures.” Terrabytes and exabytes and petabytes of data are produced by Facebook and Walmart who analyze them with complicated “machine learning algorithms” and “natural language processing.” Breathless claims (“data is in the driver’s seat” claims a recent New York Times article) and hyperventilatory rhetoric (“The end of theory” claimed Wired’s Chris Anderson) accompany these developments. The only alternative apparently, is to anxiously and darkly depict world without privacy.1 The cloud of claims about cloud computing and big data settle into recognizable, if no less nebulous fog banks of enthusiasm or anxiety.

Or consider the Arab Spring of 2011, and the anniversary of the revolution in Egypt this year. The question has repeatedly been posed as to whether the Internet, specifically social media platforms like Facebook and Twitter, had caused the revolution. Two kinds of answers typically follow.  First, the qualified yes: these technological media were necessary but not sufficient, they provided new capacities for organization that previous revolutions did not possess. Second, the concerted no: the technologies are important, but the necessary and sufficient cause of the revolution was “the people.” No one (except Biz Stone and Mark Zuckerberg) believes that these tools actually cause revolutions.2

Both answers miss the mark, but they nonetheless point to one of those well-worn paths of argument. On the one hand there are technologies that create new relationships, new capacities, or re-arrange existing relationships of knowledge and power. On the other hand, there are the reassuringly familiar collectivities—like “the people” or “the public” or “the community.” Sometimes information technologies are invoked as a threat to older forms of collective life; other times, especially in response to inflated claims about the power of those technologies, they are seen as irrelevant to the power of   known collectives. Do information technologies connect existing collectivities or do they generate the conditions of possibility for new collectivities—maybe even new kinds of collectivity?

Over the last couple of decades many observers, both scholars and journalists, have clearly sensed that there is a problem here. The problem is an unsolved one, if the proliferation of recent terminology is any indication: network societies, virtual communities, digital culture, cyber-cultures, social media, social software, digital natives, online communities, crowd-sourcing, crowd-funding, organizational networks, networked publics, and so on.

Each of these terms conjugates an apparently straightforward technological thing with an apparently straightforward collective of some kind. But the result is apparently not straightforward. Instead, each one poses anew an opposition between emergent technology and  stable collectives, strengthening the idea that the two are of different orders. In some cases, these terms are optimistic propositions that older kinds of collectivities can be intensified or expanded; in other cases (e.g. digital divides, information plantations), the conjugations point to more pessimistic conclusions.

Lurking behind such terms and debates is a much more general question. Contemporary information technology brings into relief a long-standing tension about the constitution of large-scale collectivities: namely, do they actually exist in any meaningful sense before they are constituted? Or are they artifacts of their technological intermediation? This tension between “natural” forms of community and mediation – particularly technological mediation – is one of the oldest stories that moderns tell about themselves. These collectivities need not know themselves (the way “the people” is sometimes said to); they may not even know they exist until they are shown to themselves through the operations of knowledge making and technology.

In this issue of LIMN, we asked contributors to address the problem head-on, and to consider the nature of representing and intervening in collectives. To pull apart claims about technology and collective kinds, we engaged not only scholars of the present, but also of the past.


In the 1890s, in Europe and America, a new kind of collective became an object of analysis: the crowd. The most famous diagnostician of crowds, Gustave Le Bon, constructed this concept out of a concern about civilization and its discontents: the discovery of the unconscious; the new urban realities of density, electric light, and public transport; and the eminently Victorian interest in the primitive within.

What Le Bon and others recognized was not just that people sometimes gathered together in a particular way, but that this way of gathering was tied to a particular moment in history, to a set of technologies and environmental changes and to hypothesized features of human behavior. The “crowd” was not just a horde or a mob, and it certainly was not polite society or a community.  But it was new, and it was something that needed to be studied.

Fears about the crowd gave way within a few decades to increasingly sophisticated talk about “mass society” and the values and dangers of propaganda. Similar diagnoses—from the high cultural narratives of the Frankfurt School to the handbooks of propaganda, or the strategies of the new mass medium of radio—accompanied this new collective kind. The tension was also visible in the rise of a form of market capitalism that relied on anonymity—a mode of asocial, or anonymous, sociality that would eventually become a familiar problem for marketing, demographic research and national welfare. New kinds of collectivities are linked in obscure ways to the technologies that might make relations among people real, or visible, and sometimes both.

A similar story can be told about all of the heterogonous collective kinds that feature in our world: the public, the people, the population, the nation, society, the community (both the 19th century primitive community of ethnology and 20th century voluntaristic ones of communitarianism), the demographic segment, the network, and so on (see the infographic on the following pages). All of them have to some degree been ‘naturalized’ through the varieties of cultural practices that take them for granted: the design of government; the collection of information about people, including their behaviors and biology; and the attempt to use them as heuristics for the control of large groups of people.

Such a question is likely more familiar to historians than it is to those who claim expertise about new technologies. For instance, historians of statistics like Ian Hacking, Ted Porter, Alain Derosières or Mary Poovey have very clearly described how the direct role of statistics in constituting “society” and “populations” in the 19th century. The technical characteristics of statistics coupled with national infrastructures of censuses, public health and policing called these new kinds of collectives – now taken for granted – into being. Sophisticated means of representing collectives, such as statistics, enable new forms of management, governance and intervention. They ultimately create a seemingly clear-cut concrete kind—a collective that people can occupy, analyze and ultimately govern.

Reflecting on the historical production of collective kinds can help orient and generate questions about these new phenomena—and some of the pieces in this issue provide that framing. In his piece, Alain Desrosières explains how debates about statistics after 1968 in France raised the question: “Do statistics have politics?” It pitted the “leftist” correspondence analysis (a technique used most famously in Bourdieu’s Distinction) against the “rightist” neo-classical statistical thinking. As he points out, many of the same “leftist” approaches are now at the heart of data-mining and profiling projects in “big data.” Similarly, Rebecca Lemov’s piece reflects on the kind of collective implied by the everywhere-and-nowhere device of the focus group. It emerged at the height of cold-war mass society and reflected mass society’s desires back to it, through the artificial creation of representative individuals (who Joan Didion archly referred to as those “twenty people who lived in or near Cincinnati”). Both correspondence analysis and focus groups “map the collective” in different ways—either through a clever statistical technique that integrates the aggregate with the idiosyncratic or in the case of focus groups, by creating a space in which the idiosyncratic is allowed to stand for the aggregate in a way that is simultaneously convincing and absurd.

One of the most obvious collectives aggressively produced and represented in increasingly sophisticated ways is the political body of representative democracies—alternately figured as the people, the citizenry, the public sphere, the voting public etc. Daniel Kreiss and Maria Vidart outline for us what happens when social media collides with these classic collective kinds. The authors pose a double question of control: can social media be used to control voters and campaigners during an election, and conversely can social media itself be controlled? What kind of unruly new collective does it represent and what will be its effects on the established practices of mobilizing voters and winning campaigns?

The language of “crowds”—crowdsourcing, crowdfunding, the wisdom of crowds—has become one of the dominant modes of figuring the collective at the heart of new information technologies today. It is not the same crowd of Le Bon, though a comparison would no doubt prove fascinating.  In their contributions, Alek Felstiner (a labor lawyer) and Roma Jhaveri (a former employee of crowdfunder Kiva) present theoretical and practical accounts that explain clearly what these new techniques do well and what challenges or shortcomings they face. Lilly Irani shows us the detailed workings of Amazon Mechanical Turk—one of the most successful of the crowdsourcing endeavors. In her portrait she shows how AMT both solves problems that require human labor—the kinds of things computers still can’t do—at the same time that it creates a new problem of management.

Tarleton Gillespie takes us inside (or as close as we can get to) an algorithm: the one powering “Twitter Trends.” Big data is rarely interesting as such—rather it takes on significance in the moment when it is used to display a collective to itself, whether as a visualization of something or, as in the case of Twitter Trends, as a claim about some movement or trend of a collective rather than an individual nature. The question of whether such an algorithm can be wrong is not straightforward. Indeed, can one feel strongly—much less be right or wrong—about a collective without first finding a way to show that collective what it is?

Nick Seaver’s piece also lays open the workings of big data, in his case the technique of “collaborative filtering” at the heart of software like Netflix and Amazon recommendation systems. Collaborative filtering reveals just how central—and how unquestioned—the notion of individual preference has become, and how it is being programmed into the heart of the tools we use.

Because it is so easy to look directly to social media and the Internet when asking about things like crowds and collectives, shifting the focus into different environments can reveal things overlooked.  Natasha Schüll’s contribution points us to the “touch-point collectives” of casino machine gambling.  At the forefront of consumer data gathering, the closed world of casino redesign detects, constructs, and caters to specific collectives. Paying attention to these practices can diagnose larger concerns about data, privacy, consumer behavior and the control exercised by the corporations who own the data.

Similarly, Emmanuel Didier shifts our gaze to that of the police—specifically those in the Real Time Crime Center (RTCC) of the New York Police Department. NYC’s police have gained notoriety for their use of statistics, and in particular for “comstat” which now routinely figures as a kind of artificial detective in crime dramas like The Wire. Didier shows not only how the RTCC works with data as a live stream, but also how it serves to create a form of police protection more suitable for Wall Street than other New Yorkers. Like Desrosières, Didier shows how “data mining” serves certain political purposes and not others.

Chris Csikszentmihályi steps back even further to look at how engineering education is related to the kinds of technologies and problem solving that exist today. Engineering was for most of the 20th century the province of the engineers on the inside of the universities, the defense industry or the government. But with the advent of the Internet, and especially of Free and Open Source software in the 1980s, that dominance has begun to wane—today there are collectives of amateur engineers growing everywhere, and not beholden to the demands of mainstream engineering. Csikszentmihályi shows some of what such alternative engineering collectives might achieve.

Finally, the very emblem of resistance to the creation of new collective kinds is anonymity. From the anonymous Federalist papers of an 18th century public sphere, to the presumption of anonymity in markets, to the anonymous subjects of propaganda, the un-named and un-nameable are powerful figures of critique and danger in nearly every figuration of a collective. Gabriella Coleman puts the contemporary hacker collective Anonymous on display—both to show how and where they operate, in the technically specific domain of the Internet Relay Chat network, but also to show us how her own involvement as an ethnographer (and not a journalist) buys her membership (or not) in this collective.

The collection of articles in this issue shows the depth and diversity of perspectives that can interrupt conventional accounts of the phenomena of crowds and clouds. There are (new?) collectives of people and (new?) collections of data about which we actually know very little, and there is too often a demand to speak in haste, to claim expertise on the basis of familiarity, and to rely too easily on concepts such as “society” or “community” that should also be placed in question. The race for novelty in world of information technology should be a clear occasion for pause in the world of thought; and so it is here…

Christopher M. Kelty
March 2012


About the author

Christopher M. Kelty is a professor at the University of California, Los Angeles. More »


1. Steve Lohr, “The Age of Big Data,” New York Times, Sunday Review Section, Feb. 11th 2012. Curiously, the article is illustrated by the work of Chad Hagen, who creates “fictional data visualizations” that use no data. Chris Anderson, “The End of Theory,” Wired 16(7).

2. And Wael Ghonim, whose memoir Revolution 2.0: A Memoir (Houghton Mifflin Harcourt, 2012) has garnered the most attention of this sort, though a similar kind of opposition is repeated in nearly every discussion of the Arab spring.