Collection: Platform Studies
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Platform Studies Collection
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Disinformation has been described as a threat to political discourse and public health. Even if this presumption is questionable, instruments such as criminal law or soft law have been utilised to tackle this phenomenon. Recently, technological solutions aiming to detect and remove false information, among other illicit content, have also been developed. These artificial intelligence (AI) tools have been criticised for being incapable of understanding the context in which content is shared on social media, thus causing the removal of posts that are protected by freedom of expression. However, in this short contribution, we argue that further problems arise, mostly in relation to the concepts that developers utilise to programme these systems. The Twitter policy on state-affiliated media labelling is a good example of how social media can use AI to affect accounts by relying on a questionable definition of disinformation.
This chapter discusses the emergence of the disinformation landscape that supported influence operations deployed in the Brexit referendum, a watershed development resulting from the shift in the governance of social technologies from communities of users to social media algorithms. We revisit key events such as the deployment of data-driven microtargeting in political campaigns epitomized by the Cambridge Analytica data scandal and the ensuing data lockdown enforced by social media platforms. These developments severely restricted independent research on mis/disinformation campaigns by preventing data access to social media data and rendering problematic content, including mis- and disinformation, increasingly more inscrutable and unobservable. The chapter concludes with a set of lessons learned from studying disinformation during the Brexit campaign.
The chapter traces the contested politics of data, shifting scales from the transnational to the national and local levels: from questions of the (extra-)territoriality of data, the role of jurisdictions and contested ‘technical territories’ to the concrete lived spaces where data are produced, stored, and circulated. The different contributions thus zoom in from global geopolitical struggles over digital sovereignty and hegemony over data infrastructure to local contestations over subsea cable networks and landing stations, data centres, as well as neighbourhood gentrification driven by AI-development. This multiscalar approach to data politics aims to emphasize the tensions between the abstract global logics of data circulation and the local realities of data, between historical state and corporate projects of extending data territories as a form of ‘domination’, and the localized effects of such projects, including gentrification, expropriation, and the colonial erasure of local knowledges and sovereignty. At the micro level, several of the contributors to this volume explore community activism through a case study of Montreal, where local activists oppose processes of gentrification and displacement driven by an emerging AI ecosystem meant to boost Canada’s innovation and platform economies. We home in on instances of community mapping that produce data in a fair and equitable way; data that empower communities to resist gentrification and expropriation and to support situated knowledges.
Digital platforms have expanded their scope from venture capital supported projects, towards global operations and multi-billion dollar oligopolies. However, the platform model is not entirely new. It was a common business model for newspapers, radio, and television. Before increasing commercialization through cable and satellite television, broadcasting was, for the most part, a state-supported monopoly only partially commercialized and privatized in many European countries. Production either relied on state subsidies through mandatory subscription payments in the public service model, or on advertising in the market oriented model. For example, audiences could freely listen to radio stations, while the state or the advertising industry provided funding for content production. Similarly, some digital corporations derive profits by providing advertising space for boosting sales and creating demand through the increase of consumption of products produced in other parts of the economy, obtaining their revenue from the circulation of commodities and capital in the economy as a whole. Additionally, they also produce their own means of production, such as software systems, algorithms, and AI, whose sales serve as an additional source of income and profits.
What is new about the digital platform model, compared to traditional media, is the scope and scale of gathered data, along with automated and improved analysis. Analysing big data requires technical assistance because manual analysis is not possible, or even conceivable, in any reasonable or economically viable amount of time. While the traditional media model required separate firms and an entire industry for audience and market analysis (for example, Nielsen, PwC), platforms developed entirely new markets on their own, along with the tools for analysing those same markets.
At the heart of our argument is a simple observation: there is an inherent asymmetry built into platforms. What makes them exciting at the level of social research, the real time data they unobtrusively generate as a by-product of user behaviour, can look rather sinister at the level of political economy1 (Mantello 2016, Wood and Monahan 2019). They are, as Marres (2018: 437) says, ‘an environment in two halves’. The front stage is a remarkably engaging place full of inducements, provocations and distraction while ‘a veritable army of social data scientists who monitor, measure, and seek to intervene in this behavioural theatre’ lurk behind the curtain (Marres 2018: 437). The users are known but the platform affords them little capacity to become knowing in turn (Kennedy and Moss 2015). This is what we have called, following Seymour (2019), ‘the social media machine’ and any account of digital public sociology needs to grapple with its implications in a systematic way. These are not tools we can pick up and put down at will but rather systems we can operate with and through that will simultaneously be exercising an influence over us, encouraging us to return more frequently and stay for longer when we do (Van Dijck and Poell 2018).
What Srnicek (2017) calls platform capitalism provides us with an exciting machinery for making public but it is one we are liable to be used by if we are not careful in our use of it. We offered the concepts of technological reflexivity and platform literacy to identify those characteristics necessary to thrive as public scholars under these conditions.
This introductory chapter considers the relationships of mutuality between parents and algorithms in in digital societies. It draws attention both to the inequalities of power between parents and platforms, as well as parents’ agency in navigating everyday life and parenting, as mediated increasingly by algorithms. The chapter draws upon recent advances within user-centric algorithm studies as part of broader conversations with communications, sociology, critical data studies, and related fields. It introduces the methodological framework of the project as well as the 30 parents who took part in it. The introduction provides an overview of all chapters in the book, encompassing parents’ negotiations of search algorithms, their understandings of algorithms in their children’s lives, algorithms involved in sharing and sharenting, news recommendation algorithms, parents’ algorithm literacies, and parents’ hopes and fears about their children’s algorithmic futures.
The aim of this chapter is to describe the socioeconomic shift toward ‘communicative capitalism’ and what that recognition summons from analysts. It begins with an account of how Fordism (and its standardized production, stable workforce, and the provision of adequate wages) became the predominant form of production and capital accumulation during much of the 20th century. Communicative capitalism, however, represents a new model of value generation, emphasizing the central role of communication in three central organizational practices: affect capture through emotional labor, platformization facilitated by digital infrastructures, and branding as a logic of socioeconomic life. The chapter highlights the implications of these shifts for organization studies, calling for a re-evaluation of traditional understandings of organizations as stable entities versus seeing them as fluid practices, which leads to a call for a shift towards practice-based analysis.
Criminological studies of social harms extensively document intersections of power and the production of harm, revealing how the actions of the powerful in the public and private sectors expose (typically) less powerful groups to harm, often with impunity. While this scholarship provides much needed insight into the often minimised or dismissed harms of the powerful, attention must also be paid to the agency of the victimised and the outcomes of their active efforts to resist such harms, especially in a digital context where concepts such as ‘power’ and ‘capital’ might take a different meaning. To this end, this paper expands existing criminological scholarship on social harms by providing new insights on how the dynamics of resistance by ordinary citizens, that is, people not generally considered part of the powerful capitalist elite, can nevertheless produce secondary social harms. The paper uses the example of online resistance to the COVID-19 digital tracing ‘track and trace’ app in England and Wales to unravel how ordinary citizens utilise their agency to resist the perceived harms of powerful actors while, at the same time, producing the secondary social harm of information pollution.
In this introductory chapter we address the concept of platform as it has been developed in the context of platform studies. We identify four overarching themes in the study of platforms: materiality, operations, economic framing, and governance. In extension of this we present two principles for situating platforms: historicizing and provincializing platforms. The first concerns the historically specificities of platforms in the form of predecessors and historical trajectories, while the second concerns the specific economic, social and cultural contexts that shape platforms. We argue that applying these dimension to the study of platforms bring attention characteristics and phenomena that otherwise remain hidden. Finally, we present the individual chapters of the volume and how they play into our ambition of historicizing and provincializing platforms.
The COVID-19 pandemic, urban lockdown containment measures and national economic crises are impacting in a large way both urban spaces and platform labor. Cities are transforming the way to live in, while platforms are adapting to these new background conditions.
In this chapter we provide an overview on how platform labor was affected by COVID-19 and the implications this brings for urban planning and policies in the next months.1 We will make use of some results from the Horizon 2020 PLUS project (Platform Labour in Urban Spaces) investigating four main disruptive platforms (Uber, Airbnb, Helpling,2 and Deliveroo) in seven European cities (Barcelona, Berlin, Bologna, Lisbon, London, Paris, Tallinn).
First, we will introduce the effects of platform economy on labor and cities. Second, we will focus on specific case studies framed in a trans-urban comparison to highlight commonalities and differences in platform labor transformations and urban challenges. Finally, we will consider implications for urban policy, highlighting the potential role of local administrations in platforms’ management.
Platforms are ubiquitous in the contemporary world. In the time of the COVID-19 pandemic this is more apparent than ever. Just think of the use of such platforms like Zoom or Teams not only for work purposes but also to manage social relationships during the lockdown, and the expansion of app-driven food delivery, let alone the further entrenchment of the operations of an inventory giant like Amazon, which has become a full-fledged logistics company. The profits and stock market value of many platforms have consequently boomed in a spectacular way, and it is safe to predict that they will emerge from the current crisis as winners.
