Year:

  1.  1
    Understanding the Meaning of Emoji in Mobile Social Payments: Exploring the Use of Mobile Payments as Hedonic Versus Utilitarian Through Skin Tone Modified Emoji Usage.Amelia Acker, Clive Unger, Ishank Arora, Wei-Jie Xiao, Pratik Shah, Charulata Ghosh, Jung-Ah Lee, Sabitha Sudarshan & Dhiraj Murthy - 2020 - Big Data and Society 7 (2).
    Despite research establishing emojis as sites of critical racial discourse, there is a paucity of literature examining their importance in the increasingly popular context of mobile payments. This is particularly important as new forms of social payment platforms such as Venmo bridge the seamlessness of mobile payments with the vibrant communicative practices of social networks. As such, they provide a unique medium to examine how emojis are used within the context of digital consumption, and by extension, self-representation. This study analyzes (...)
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  2.  1
    The “Black Box” at Work.Ifeoma Ajunwa - 2020 - Big Data and Society 7 (2).
    An oversized reliance on big data-driven algorithmic decision-making systems, coupled with a lack of critical inquiry regarding such systems, combine to create the paradoxical “black box” at work. The “black box” simultaneously demands a higher level of transparency from the worker in regard to data collection, while shrouding the decision-making in secrecy, making employer decisions even more opaque to the worker. To access employment, the worker is commanded to divulge highly personal information, and when hired, must submit further still to (...)
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  3. Emerging Models of Data Governance in the Age of Datafication.Anna Berti Suman, Max Craglia, Marisa Ponti & Marina Micheli - 2020 - Big Data and Society 7 (2).
    The article examines four models of data governance emerging in the current platform society. While major attention is currently given to the dominant model of corporate platforms collecting and economically exploiting massive amounts of personal data, other actors, such as small businesses, public bodies and civic society, take also part in data governance. The article sheds light on four models emerging from the practices of these actors: data sharing pools, data cooperatives, public data trusts and personal data sovereignty. We propose (...)
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  4. Good Organizational Reasons for Better Medical Records: The Data Work of Clinical Documentation Integrity Specialists.Claus Bossen & Kathleen H. Pine - 2020 - Big Data and Society 7 (2).
    Healthcare organizations and workers are under pressure to produce increasingly complete and accurate data for multiple data-intensive endeavors. However, little research has examined the emerging occupations arising to carry out the data work necessary to produce “improved” data sets, or the specific work activities of these emerging data occupations. We describe the work of Clinical Documentation Integrity Specialists, an emerging occupation that focuses on improving clinical documentation to produce more detailed and accurate administrative datasets crucial for evolving data-intensive forms of (...)
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  5. Black Boxes, Not Green: Mythologizing Artificial Intelligence and Omitting the Environment.Benedetta Brevini - 2020 - Big Data and Society 7 (2).
    We are repeatedly told that AI will help us to solve some of the world's biggest challenges, from treating chronic diseases and reducing fatality rates in traffic accidents to fighting climate change and anticipating cybersecurity threats. However, the article contends that public discourse on AI systematically avoids considering AI’s environmental costs. Artificial Intelligence- Brevini argues- runs on technology, machines, and infrastructures that deplete scarce resources in their production, consumption, and disposal, thus increasing the amounts of energy in their use, and (...)
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  6. Intentionality and Design in the Data Sonification of Social Issues.Paolo Ciuccarelli & Sara Lenzi - 2020 - Big Data and Society 7 (2).
    Data sonification is a practice for conducting scientific analysis through the use of sound to represent data. It is now transitioning to a practice for communicating and reaching wider publics by expanding the range of languages and senses for understanding complexity in data-intensive societies. Communicating to wider publics, though, requires that authors intentionally shape sonification in ways that consider the goals and contexts in which publics relate. It requires a specific set of knowledge and skills that design as a discipline (...)
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  7. Disruption and Dislocation in Post-COVID Futures for Digital Health.Alessia Costa & Richard Milne - 2020 - Big Data and Society 7 (2).
    In this piece we explore the COVID pandemic as an opportunity for the articulation and realization of digital health futures. Our discussion draws on an engagement with emergent discourse around COVID-19 and ongoing work on imaginaries of future care associated with digital tools for the detection of cognitive decline and the risk of dementia. We describe how the post-COVID futures of digital health are narrated in terms of the timing and speed with which they are being brought into being, as (...)
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  8. Big Data: From Modern Fears to Enlightened and Vigilant Embrace of New Beginnings.Nicole Dewandre - 2020 - Big Data and Society 7 (2).
    In The Black Box Society, Frank Pasquale develops a critique of asymmetrical power: corporations’ secrecy is highly valued by legal orders, but persons’ privacy is continually invaded by these corporations. This response proceeds in three stages. I first highlight important contributions of The Black Box Society to our understanding of political and legal relationships between persons and corporations. I then critique a key metaphor in the book, and the role of transparency and ‘watchdogging’ in its primary policy prescriptions. I then (...)
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  9.  1
    Covid-19 and the Accelerating Smart Home.Robyn Dowling & Sophia Maalsen - 2020 - Big Data and Society 7 (2).
    Home, digital technologies and data are intersecting in new ways as responses to the COVID-19 pandemic emerge. We consider the data practices associated with COVID-19 responses and their implications for housing and home through two overarching themes: the notion of home as a private space, and digital technology and surveillance in the home. We show that although home has never been private, the rapid adoption and acceptance of technologies in the home for quarantine, work and study, enabled by the pandemic, (...)
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  10.  1
    Contesting Algorithms: Restoring the Public Interest in Content Filtering by Artificial Intelligence.Niva Elkin-Koren - 2020 - Big Data and Society 7 (2).
    In recent years, artificial intelligence has been deployed by online platforms to prevent the upload of allegedly illegal content or to remove unwarranted expressions. These systems are trained to spot objectionable content and to remove it, block it, or filter it out before it is even uploaded. Artificial intelligence filters offer a robust approach to content moderation which is shaping the public sphere. This dramatic shift in norm setting and law enforcement is potentially game-changing for democracy. Artificial intelligence filters carry (...)
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  11. From Pool to Profile: Social Consequences of Algorithmic Prediction in Insurance.Elena Esposito & Alberto Cevolini - 2020 - Big Data and Society 7 (2).
    The use of algorithmic prediction in insurance is regarded as the beginning of a new era, because it promises to personalise insurance policies and premiums on the basis of individual behaviour and level of risk. The core idea is that the price of the policy would no longer refer to the calculated uncertainty of a pool of policyholders, with the consequence that everyone would have to pay only for her real exposure to risk. For insurance, however, uncertainty is not only (...)
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  12. Seven Intersectional Feminist Principles for Equitable and Actionable COVID-19 Data.Lauren F. Klein & Catherine D'Ignazio - 2020 - Big Data and Society 7 (2).
    This essay offers seven intersectional feminist principles for equitable and actionable COVID-19 data, drawing from the authors' prior work on data feminism. Our book, Data Feminism, offers seven principles which suggest possible points of entry for challenging and changing power imbalances in data science. In this essay, we offer seven sets of examples, one inspired by each of our principles, for both identifying existing power imbalances with respect to the impact of the novel coronavirus and its response, and for beginning (...)
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  13.  1
    A Proxy for Privacy Uncovering the Surveillance Ecology of Mobile Apps.Sofie Flensburg & Signe Sophus Lai - 2020 - Big Data and Society 7 (2).
    The article develops a methodological and empirical approach for gauging the ways Big Data can be collected and distributed through mobile apps. This approach focuses on the infrastructural components that condition the disclosure of smartphone users’ data – namely the permissions that apps request and the third-party corporations they work with. We explore the surveillance ecology of mobile apps and thereby the privacy implications of everyday smartphone use through three analytical perspectives: The first focuses on the ‘appscapes’ of individual smartphone (...)
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  14. The Digital Life of the #Migrantcaravan: Contextualizing Twitter as a Spatial Technology.Emmanuel Frimpong Boamah & Margath A. Walker - 2020 - Big Data and Society 7 (2).
    The Central American migrant caravans of 2018 are best understood as having been precipitated by entangled multi-scalar geopolitical histories among the United States, Mexico, Guatemala, Honduras, and El Salvador. Unsurprisingly, the migrants traveling north to the United States garnered widespread attention on social media. So much so that the reaction to the caravan accelerated plans to deploy troops to the US southern border and deny Central Americans the opportunity to seek asylum. This example showcases how the digital world can have (...)
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  15.  1
    Content Moderation, AI, and the Question of Scale.Tarleton Gillespie - 2020 - Big Data and Society 7 (2).
    AI seems like the perfect response to the growing challenges of content moderation on social media platforms: the immense scale of the data, the relentlessness of the violations, and the need for human judgments without wanting humans to have to make them. The push toward automated content moderation is often justified as a necessary response to the scale: the enormity of social media platforms like Facebook and YouTube stands as the reason why AI approaches are desirable, even inevitable. But even (...)
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  16. The Sale of Heritage on eBay: Market Trends and Cultural Value.Tasoula Georgiou Hadjitofi & Mark Altaweel - 2020 - Big Data and Society 7 (2).
    The marketisation of heritage has been a major topic of interest among heritage specialists studying how the online marketplace shapes sales. Missing from that debate is a large-scale analysis seeking to understand market trends on popular selling platforms such as eBay. Sites such as eBay can inform what heritage items are of interest to the wider public, and thus what is potentially of greater cultural value, while also demonstrating monetary value trends. To better understand the sale of heritage on eBay’s (...)
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  17.  2
    Editorial: The Personalisation of Insurance: Data, Behaviour and Innovation.Ine Van Hoyweghen, Gert Meyers & Liz McFall - 2020 - Big Data and Society 7 (2).
    The adoption of Big Data analytics in insurance has proved controversial but there has been little analysis specifying how insurance practices are changing. Is insurance passively subject to the forces of disruptive innovation, moving away from the pooling of risk towards its personalisation or individualisation, and what might that mean in practice? This special theme situates disruptive innovations, particularly the experimental practices of behaviour-based personalisation, in the context of the practice and regulation of contemporary insurance. Our contributors argue that behaviour-based (...)
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  18. Cambridge Analytica’s Black Box.Margaret Hu - 2020 - Big Data and Society 7 (2).
    The Cambridge Analytica–Facebook scandal led to widespread concern over the methods deployed by Cambridge Analytica to target voters through psychographic profiling algorithms, built upon Facebook user data. The scandal ultimately led to a record-breaking $5 billion penalty imposed upon Facebook by the Federal Trade Commission in July 2019. The FTC action, however, has been criticized as failing to adequately address the privacy and other harms emanating from Facebook’s release of approximately 87 million Facebook users’ data, which was exploited without user (...)
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  19. A Qualitative Analysis of Sarcasm, Irony and Related #Hashtags on Twitter.Thomas W. Jackson, Suzanne Elayan & Martin Sykora - 2020 - Big Data and Society 7 (2).
    As the use of automated social media analysis tools surges, concerns over accuracy of analytics have increased. Some tentative evidence suggests that sarcasm alone could account for as much as a 50% drop in accuracy when automatically detecting sentiment. This paper assesses and outlines the prevalence of sarcastic and ironic language within social media posts. Several past studies proposed models for automatic sarcasm and irony detection for sentiment analysis; however, these approaches result in models trained on training data of highly (...)
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  20. Epistemic Clashes in Network Science: Mapping the Tensions Between Idiographic and Nomothetic Subcultures.Mathieu Jacomy - 2020 - Big Data and Society 7 (2).
    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 (...)
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  21.  1
    Mass Personalization: Predictive Marketing Algorithms and the Reshaping of Consumer Knowledge.Baptiste Kotras - 2020 - Big Data and Society 7 (2).
    This paper focuses on the conception and use of machine-learning algorithms for marketing. In the last years, specialized service providers as well as in-house data scientists have been increasingly using machine learning to predict consumer behavior for large companies. Predictive marketing thus revives the old dream of one-to-one, perfectly adjusted selling techniques, now at an unprecedented scale. How do predictive marketing devices change the way corporations know and model their customers? Drawing from STS and the sociology of quantification, I propose (...)
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  22.  2
    Disrupting the Library: Digital Scholarship and Big Data at the National Library of Scotland.Stuart Lewis & Sarah Ames - 2020 - Big Data and Society 7 (2).
    With a mass digitisation programme underway and the addition of non-print legal deposit and web archive collections, the National Library of Scotland is now both producing and collecting data at an unprecedented rate, with over 5PB of storage in the Library’s data centres. As well as the opportunities to support large scale analysis of the collections, this also presents new challenges around data management, storage, rights, formats, skills and access. Furthermore, by assuming the role of both creators and collectors, libraries (...)
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  23.  25
    Going Viral: How a Single Tweet Spawned a COVID-19 Conspiracy Theory on Twitter.Philip Mai & Anatoliy Gruzd - 2020 - Big Data and Society 7 (2).
    In late March of 2020, a new hashtag, #FilmYourHospital, made its first appearance on social media. The hashtag encouraged people to visit local hospitals to take pictures and videos of empty hospitals to help “prove” that the COVID-19 pandemic is an elaborate hoax. Using techniques from Social Network Analysis, this case study examines how this conspiracy theory propagated on Twitter and whether the hashtag virality was aided by the use of automation or coordination among Twitter users. We found that while (...)
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  24. For a Situational Analytics: An Interpretative Methodology for the Study of Situations in Computational Settings.Noortje Marres - 2020 - Big Data and Society 7 (2).
    This article introduces an interpretative approach to the analysis of situations in computational settings called situational analytics. I outline the theoretical and methodological underpinnings of this approach, which is still under development, and show how it can be used to surface situations from large data sets derived from online platforms such as YouTube. Situational analytics extends to computationally-mediated settings a qualitative methodology developed by Adele Clarke, Situational Analysis, which uses data mapping to detect heterogeneous entities in fieldwork data to determine (...)
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  25. COVID-19: What Does It Mean for Digital Social Protection?Silvia Masiero - 2020 - Big Data and Society 7 (2).
    COVID-19 has hit a world in which social protection schemes are increasingly augmented with digital measures. Digital identity schemes are especially being adopted to match citizens’ data with social protection entitlements, enabling authentication through demographic and, increasingly, biometric data at the point of access. In this commentary, I discuss three sets of implications that COVID-19 has yielded on digital social protection, whose central trade-off – increasing the probabilities of accurate user identification, at the cost of greater exclusions – has become (...)
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  26. Learning From Lines: Critical COVID Data Visualizations and the Quarantine Quotidian.Shannon Mattern, Erin Simmons & Emily Bowe - 2020 - Big Data and Society 7 (2).
    In response to the ubiquitous graphs and maps of COVID-19, artists, designers, data scientists, and public health officials are teaming up to create counter-plots and subaltern maps of the pandemic. In this intervention, we describe the various functions served by these projects. First, they offer tutorials and tools for both dataviz practitioners and their publics to encourage critical thinking about how COVID-19 data is sourced and modeled—and to consider which subjects are not interpellated in those data sets, and why not. (...)
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  27. The Value of Sharing: Branding and Behaviour in a Life and Health Insurance Company.Liz McFall & Hugo Jeanningros - 2020 - Big Data and Society 7 (2).
    As Big Data, the Internet of Things and insurance collide, so too, do the best and the worst of our futures. Insurance is summoned as an example of the interference in our private lives that is already underway everywhere. In this paper, we pause to reflect on this argument. Can changes in the way insurance measures the value of behaviour really serve as an example of the individual and social harms of datafication? How do we know? Insurance is a mathematical (...)
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  28. Techno-Solutionism and the Standard Human in the Making of the COVID-19 Pandemic.Stefania Milan - 2020 - Big Data and Society 7 (2).
    Quantification is particularly seductive in times of global uncertainty. Not surprisingly, numbers, indicators, categorizations, and comparisons are central to governmental and popular response to the COVID-19 pandemic. This essay draws insights from critical data studies, sociology of quantification and decolonial thinking, with occasional excursion into the biomedical domain, to investigate the role and social consequences of counting broadly defined as a way of knowing about the virus. It takes a critical look at two domains of human activity that play a (...)
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  29.  1
    “No Disease for the Others”: How COVID-19 Data Can Enact New and Old Alterities.Annalisa Pelizza - 2020 - Big Data and Society 7 (2).
    The COVID-19 pandemic invites a question about how long-standing narratives of alterity and current narratives of disease are entwined and re-enacted in the diagnosis of COVID-19. In this commentary, we discuss two related phenomena that, we argue, should be taken into account in answering this question. First, we address the diffusion of pseudoscientific accounts of minorities’ immunity to COVID-19. While apparently praising minorities’ biological resistance, such accounts rhetorically introduce a distinction between “Us” and “Them,” and in so doing produce new (...)
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  30. AI Ethics Should Not Remain Toothless! A Call to Bring Back the Teeth of Ethics.Rowena Rodrigues & Anaïs Rességuier - 2020 - Big Data and Society 7 (2).
    Ethics has powerful teeth, but these are barely being used in the ethics of AI today – it is no wonder the ethics of AI is then blamed for having no teeth. This article argues that ‘ethics’ in the current AI ethics field is largely ineffective, trapped in an ‘ethical principles’ approach and as such particularly prone to manipulation, especially by industry actors. Using ethics as a substitute for law risks its abuse and misuse. This significantly limits what ethics can (...)
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  31.  1
    The Birth of Sensory Power: How a Pandemic Made It Visible?Evelyn Ruppert & Engin Isin - 2020 - Big Data and Society 7 (2).
    Much has been written about data politics in the last decade, which has generated myriad concepts such as ‘surveillance capitalism’, ‘gig economy’, ‘quantified self’, ‘algorithmic governmentality’, ‘data colonialism’, ‘data subjects’ and ‘digital citizens’. Yet, it has been difficult to plot these concepts into an historical series to discern specific continuities and discontinuities since the origins of modern power in its three major forms: sovereign, disciplinary and regulatory. This article argues that the coronavirus pandemic in 2020 brought these three forms of (...)
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  32. “Smittestopp”: If You Want Your Freedom Back, Download Now.Kristin B. Sandvik - 2020 - Big Data and Society 7 (2).
    The intervention attempts to engage critically with the Smittestopp app as a specifically Norwegian technofix. Culturally and politically, much of the Covid-19 response and the success of social distancing rules have been organized around the widespread trust in the government and public health authorities, and a focus on the citizens’ duty to contribute to the dugnaðr. The intervention argues that Smittestopp has been co-created by the mobilization of trust and dugnaðr, resulting in the launch of an incomplete and poorly defined (...)
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  33.  1
    Innovation Under Pressure: Implications for Data Privacy During the Covid-19 Pandemic.Gil Scheitlin, Rehana Harasgama, Eduard Fosch Villaronga, Aurelia Tamò-Larrieux, Christoph Lutz & Gemma Newlands - 2020 - Big Data and Society 7 (2).
    The global Covid-19 pandemic has resulted in social and economic disruption unprecedented in the modern era. Many countries have introduced severe measures to contain the virus, including travel restrictions, public event bans, non-essential business closures and remote work policies. While digital technologies help governments and organizations to enforce protection measures, such as contact tracing, their rushed deployment and adoption also raises profound concerns about surveillance, privacy and data protection. This article presents two critical cases on digital surveillance technologies implemented during (...)
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  34.  3
    Making Data Science Systems Work.Phoebe Sengers & Samir Passi - 2020 - Big Data and Society 7 (2).
    How are data science systems made to work? It may seem that whether a system works is a function of its technical design, but it is also accomplished through ongoing forms of discretionary work by many actors. Based on six months of ethnographic fieldwork with a corporate data science team, we describe how actors involved in a corporate project negotiated what work the system should do, how it should work, and how to assess whether it works. These negotiations laid the (...)
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  35.  1
    A Post-Truth Pandemic?Taylor Shelton - 2020 - Big Data and Society 7 (2).
    As the coronavirus pandemic continues apace in the United States, the dizzying amount of data being generated, analyzed and consumed about the virus has led to calls to proclaim this the first ‘data-driven pandemic’. But at the same time, it seems that this plethora of data has not meant a better grasp on the reality of the pandemic and its effects. Even as we have the potential to digitally track and trace nearly every single individual who has contracted the virus, (...)
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  36. The Politics of Algorithmic Governance in the Black Box City.Gavin J. D. Smith - 2020 - Big Data and Society 7 (2).
    Everyday surveillance work is increasingly performed by non-human algorithms. These entities can be conceptualised as machinic flâneurs that engage in distanciated flânerie: subjecting urban flows to a dispassionate, calculative and expansive gaze. This paper provides some theoretical reflections on the nascent forms of algorithmic practice materialising in two Australian cities, and some of their implications for urban relations and social justice. It looks at the idealisation – and operational black boxing – of automated watching programs, before considering their impacts on (...)
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  37. Caution: Rumors Ahead—A Case Study on the Debunking of False Information on Twitter.Stefan Stieglitz, Björn Ross & Anna-Katharina Jung - 2020 - Big Data and Society 7 (2).
    As false information may spread rapidly on social media, a profound understanding of how it can be debunked is required. This study offers empirical insights into the development of rumors after they are debunked, the various user groups who are involved in the process, and their network structures. As crisis situations are highly sensitive to the spread of rumors, Twitter posts from during the 2017 G20 summit are examined. Tweets regarding five rumors that were debunked during this event were manually (...)
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  38.  1
    Contested Technology: Social Scientific Perspectives of Behaviour-Based Insurance.Maiju Tanninen - 2020 - Big Data and Society 7 (2).
    In this review, I analyse how ‘behaviour-based personalisation’ in insurance – that is, insurers’ increased interest in tracking and manipulating insureds’ behaviour with, for instance, wearable devices – has been approached in recent social scientific literature. In the review, I focus on two streams of literature, critical data studies and the sociology of insurance, discussing the new insurance schemes that utilise sensor-generated and digital data. The aim of this review is to compare these two approaches and to analyse what kinds (...)
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  39. Data/Infrastructure in the Smart City: Understanding the Infrastructural Power of Citymapper App Through Technicity of Data.Güneş Tavmen - 2020 - Big Data and Society 7 (2).
    Over the last few years, smart cities have been a focus of scholarly attention. Most of these critical studies concentrated on the multinational corporations’ discourses and their implications on urban policies. Besides these factors, however, the data-driven city develops within a complex web of entanglements whereby data-driven technologies modulate the urban infrastructure in a multitude of ways contingent upon the social, political, material and technical aspects. As such, this article attends to the infrastructural implications of a smart city product, Citymapper, (...)
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  40. The Price of Certainty: How the Politics of Pandemic Data Demand an Ethics of Care.Linnet Taylor - 2020 - Big Data and Society 7 (2).
    The Covid-19 pandemic broke on a world whose grip on epistemic trust was already in disarray. The first months of the pandemic saw many governments publicly performing reliance on epidemiological and modelling expertise in order to signal that data would be the basis for justifying whatever population-level measures of control were judged necessary. But comprehensive data has not become available, and instead scientists, policymakers and the public find themselves in a situation where policy inputs determine the data available and vice (...)
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  41. Data-Bodies and Data Activism: Presencing Women in Digital Heritage Research.Terrie Lynn Thompson - 2020 - Big Data and Society 7 (2).
    As heritage-as-the-already-occurred folds into heritage-in-the-making practices, temporal and spatial fluidity is made more complex by digital mediation and particularly by Big Data. Such liveliness evokes ontological, epistemological and methodological challenges. Drawing on more-than-human theorizing, this article reframes the notion of data-bodies to advance data activist-oriented research in heritage. Focused primarily on women, it examines how their distributed agency and voice with respect to data practices and the makings of heritage could be amplified. I describe three methodological directions, influenced by feminist (...)
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  42.  1
    COVID-19 is Spatial: Ensuring That Mobile Big Data is Used for Social Good.Tuuli Toivonen, Matthew Zook, Olle Järv & Age Poom - 2020 - Big Data and Society 7 (2).
    The mobility restrictions related to COVID-19 pandemic have resulted in the biggest disruption to individual mobilities in modern times. The crisis is clearly spatial in nature, and examining the geographical aspect is important in understanding the broad implications of the pandemic. The avalanche of mobile Big Data makes it possible to study the spatial effects of the crisis with spatiotemporal detail at the national and global scales. However, the current crisis also highlights serious limitations in the readiness to take the (...)
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  43.  3
    Designing for Human Rights in AI.Jeroen van den Hoven & Evgeni Aizenberg - 2020 - Big Data and Society 7 (2).
    In the age of Big Data, companies and governments are increasingly using algorithms to inform hiring decisions, employee management, policing, credit scoring, insurance pricing, and many more aspects of our lives. Artificial intelligence systems can help us make evidence-driven, efficient decisions, but can also confront us with unjustified, discriminatory decisions wrongly assumed to be accurate because they are made automatically and quantitatively. It is becoming evident that these technological developments are consequential to people’s fundamental human rights. Despite increasing attention to (...)
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  44.  1
    Viral Data.Matthew Zook & Agnieszka Leszczynski - 2020 - Big Data and Society 7 (2).
    We are experiencing a historical moment characterized by unprecedented conditions of virality: a viral pandemic, the viral diffusion of misinformation and conspiracy theories, the viral momentum of ongoing Hong Kong protests, and the viral spread of #BlackLivesMatter demonstrations and related efforts to defund policing. These co-articulations of crises, traumas, and virality both implicate and are implicated by big data practices occurring in a present that is pervasively mediated by data materialities, deeply rooted dataist ideologies that entrench processes of datafication as (...)
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  45.  1
    Cyborg Finance Mirrors Cyborg Social Media.Kamel Ajji - 2020 - Big Data and Society 7 (1).
    This article aims at showing the similarities between the financial and the tech sectors in their use and reliance on information and algorithms and how such dependency affects their attitude towards regulation. Drawing on Pasquale’s recommendations for reform, it sets out a proposal for a constant and independent scrutiny of internet service providers.
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  46.  1
    Interpretation as Luxury: Heart Patients Living with Data Doubt, Hope, and Anxiety.Tariq Osman Andersen, Henriette Langstrup & Stine Lomborg - 2020 - Big Data and Society 7 (1).
    Personal health technologies such as apps and wearables that generate health and behavior data close to the individual patient are envisioned to enable personalized healthcare - and self-care. And yet, they are consumer devices. Proponents of these devices presuppose that measuring will be helpful, and that data will be meaningful. However, a growing body of research suggests that self-tracking data does not necessarily make sense to users. Drawing together data studies and digital health research, we aim to further research on (...)
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  47. Shareable and Un-Sharable Knowledge.Mark Andrejevic - 2020 - Big Data and Society 7 (1).
    This article focuses on what it means to generate actionable but non-sharable information, and how this might relate to our understanding of what counts as knowledge, which typically entails some form of explanation. As automated systems sort and classify us for the purposes of dating, education, employment, health care, security, and more, we are going to want to know how and why these decisions are being made. Or, failing that, we will at least want to know, with as much clarity (...)
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  48.  1
    Personalization as a Promise: Can Big Data Change the Practice of Insurance?Arthur Charpentier & Laurence Barry - 2020 - Big Data and Society 7 (1).
    The aim of this article is to assess the impact of Big Data technologies for insurance ratemaking, with a special focus on motor products.The first part shows how statistics and insurance mechanisms adopted the same aggregate viewpoint. It made visible regularities that were invisible at the individual level, further supporting the classificatory approach of insurance and the assumption that all members of a class are identical risks. The second part focuses on the reversal of perspective currently occurring in data analysis (...)
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  49. Disambiguating the Benefits and Risks From Public Health Data in the Digital Economy.Sarah Cheung - 2020 - Big Data and Society 7 (1).
    This article focuses on key roles that the ill-defined concept of ‘public benefit’ plays in accessing the public health data held by the UK’s National Health Service. Using the concept of the ‘trade-off fallacy’, this article argues that current data access and governance structures, based on particular construals of public benefit in the context of public health data, largely negate the possibility of effective control by individuals over future uses of personal health data. This generates a health data version of (...)
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  50.  6
    The Trainer, the Verifier, the Imitator: Three Ways in Which Human Platform Workers Support Artificial Intelligence.Marion Coville, Antonio A. Casilli & Paola Tubaro - 2020 - Big Data and Society 7 (1).
    This paper sheds light on the role of digital platform labour in the development of today’s artificial intelligence, predicated on data-intensive machine learning algorithms. Focus is on the specific ways in which outsourcing of data tasks to myriad ‘micro-workers’, recruited and managed through specialized platforms, powers virtual assistants, self-driving vehicles and connected objects. Using qualitative data from multiple sources, we show that micro-work performs a variety of functions, between three poles that we label, respectively, ‘artificial intelligence preparation’, ‘artificial intelligence verification’ (...)
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  51.  2
    Perils of Data-Driven Equity: Safety-Net Care and Big Data’s Elusive Grasp on Health Inequality.Taylor M. Cruz - 2020 - Big Data and Society 7 (1).
    Large-scale data systems are increasingly envisioned as tools for justice, with big data analytics offering a key opportunity to advance health equity. Health systems face growing public pressure to collect data on patient “social factors,” and advocates and public officials seek to leverage such data sources as a means of system transformation. Despite the promise of this “data-driven” strategy, there is little empirical work that examines big data in action directly within the sites of care expected to transform. In this (...)
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  52. Data as Performance – Showcasing Cities Through Open Data Maps.Morgan Currie - 2020 - Big Data and Society 7 (1).
    This article describes how the City of Los Angeles is showcasing data-driven services to the public through dynamic visualisations of open data. I frame an analysis of this aspect of datafication in local government through linguistics and cultural theory; drawing on this set of literature I theorise the use of public data as both a performative tool and a performance of data-driven city services. I then discuss examples of interactive maps on the City of Los Angeles’ open data websites, produced (...)
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  53.  1
    Smart Forests and Data Practices: From the Internet of Trees to Planetary Governance.Jennifer Gabrys - 2020 - Big Data and Society 7 (1).
    Environments are increasingly becoming technologized sites of data production. From smart cities to smart forests, digital networks are analyzing and joining up environmental processes. This commentary focuses on one such understudied smart environment, smart forests, as emerging digital infrastructures that are materializing to manage and mitigate environmental change. How does the digitalization of forests not only change understandings of these environments but also generate different practices and ontologies for addressing environmental change? I first analyze smart forests within the expanding area (...)
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  54. The Virtue of Simplicity: On Machine Learning Models in Algorithmic Trading.Kristian Bondo Hansen - 2020 - Big Data and Society 7 (1).
    Machine learning models are becoming increasingly prevalent in algorithmic trading and investment management. The spread of machine learning in finance challenges existing practices of modelling and model use and creates a demand for practical solutions for how to manage the complexity pertaining to these techniques. Drawing on interviews with quants applying machine learning techniques to financial problems, the article examines how these people manage model complexity in the process of devising machine learning-powered trading algorithms. The analysis shows that machine learning (...)
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  55. How Biased is the Sample? Reverse Engineering the Ranking Algorithm of Facebook’s Graph Application Programming Interface.Justin Chun-Ting Ho - 2020 - Big Data and Society 7 (1).
    Facebook research has proliferated during recent years. However, since November 2017, Facebook has introduced a new limitation on the maximum amount of page posts retrievable through their Graph application programming interface, while there is limited documentation on how these posts are selected. This paper compares two datasets of the same Facebook page, a full dataset obtained before the introduction of the limitation and a partial dataset obtained after, and employs bootstrapping technique to assess the bias caused by the new limitation. (...)
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  56. ‘Happy Failures’: Experimentation with Behaviour-Based Personalisation in Car Insurance.Ine Van Hoyweghen & Gert Meyers - 2020 - Big Data and Society 7 (1).
    Insurance markets have always relied on large amounts of data to assess risks and price their products. New data-driven technologies, including wearable health trackers, smartphone sensors, predictive modelling and Big Data analytics, are challenging these established practices. In tracking insurance clients’ behaviour, these innovations promise the reduction of insurance costs and more accurate pricing through the personalisation of premiums and products. Building on insights from the sociology of markets and Science and Technology Studies, this article investigates the role of economic (...)
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  57.  4
    Public Perceptions of Good Data Management: Findings From a UK-Based Survey.Rhianne Jones, Robin Steedman, Helen Kennedy & Todd Hartman - 2020 - Big Data and Society 7 (1).
    Low levels of public trust in data practices have led to growing calls for changes to data-driven systems, and in the EU, the General Data Protection Regulation provides a legal motivation for such changes. Data management is a vital component of data-driven systems, but what constitutes ‘good’ data management is not straightforward. Academic attention is turning to the question of what ‘good data’ might look like more generally, but public views are absent from these debates. This paper addresses this gap, (...)
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  58.  8
    Algorithmic Content Moderation: Technical and Political Challenges in the Automation of Platform Governance.Christian Katzenbach, Reuben Binns & Robert Gorwa - 2020 - Big Data and Society 7 (1).
    As government pressure on major technology companies builds, both firms and legislators are searching for technical solutions to difficult platform governance puzzles such as hate speech and misinformation. Automated hash-matching and predictive machine learning tools – what we define here as algorithmic moderation systems – are increasingly being deployed to conduct content moderation at scale by major platforms for user-generated content such as Facebook, YouTube and Twitter. This article provides an accessible technical primer on how algorithmic moderation works; examines some (...)
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  59.  1
    Expectations of Artificial Intelligence and the Performativity of Ethics: Implications for Communication Governance.John D. Kelleher, Marguerite Barry & Aphra Kerr - 2020 - Big Data and Society 7 (1).
    This article draws on the sociology of expectations to examine the construction of expectations of ‘ethical AI’ and considers the implications of these expectations for communication governance. We first analyse a range of public documents to identify the key actors, mechanisms and issues which structure societal expectations around artificial intelligence and an emerging discourse on ethics. We then explore expectations of AI and ethics through a survey of members of the public. Finally, we discuss the implications of our findings for (...)
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  60.  4
    A Maussian Bargain: Accumulation by Gift in the Digital Economy.Daniel N. Kluttz & Marion Fourcade - 2020 - Big Data and Society 7 (1).
    The harvesting of data about people, organizations, and things and their transformation into a form of capital is often described as a process of “accumulation by dispossession,” a pervasive loss of rights buttressed by predatory practices and legal violence. Yet this argument does not square well with the fact that enrollment into digital systems is often experienced as a much more benign process: signing up for a “free” service, responding to a “friend’s” invitation, or being encouraged to “share” content. In (...)
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  61.  1
    The Old in the New: Voter Surveillance in Political Clientelism and Datafied Campaigning.Isabel Kusche - 2020 - Big Data and Society 7 (1).
    This article compares political clientelism and datafied campaigning as two modes of relating politicians/parties and voters that are centred around voter surveillance. It contributes to the discussion on consequences of Big Data by showing similarities of datafied campaigns with a type of electoral politics that pre-dates the advent of mass media and is usually regarded as deficient. It thus departs from the predominant perspective on datafication and surveillance, which draws on Foucault, in order to identify the particular challenges that datafication (...)
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  62. Plastic Surveillance: Payment Cards and the History of Transactional Data, 1888 to Present.Josh Lauer - 2020 - Big Data and Society 7 (1).
    Modern payment cards encompass a bewildering array of consumer technologies, from credit and debit cards to stored-value and loyalty cards. But what unites all of these financial media is their connection to recordkeeping systems. Each swipe sends data hurtling through invisible infrastructures to verify accounts, record purchase details, exchange funds, and update balances. With payment cards, banks and merchants have been able to amass vast archives of transactional data. This information is a valuable asset in itself. It can be used (...)
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  63.  1
    No Amount of “AI” in Content Moderation Will Solve Filtering’s Prior-Restraint Problem.Emma J. Llansó - 2020 - Big Data and Society 7 (1).
    Contemporary policy debates about managing the enormous volume of online content have taken a renewed focus on upload filtering, automated detection of potentially illegal content, and other “proactive measures”. Often, policymakers and tech industry players invoke artificial intelligence as the solution to complex challenges around online content, promising that AI is a scant few years away from resolving everything from hate speech to harassment to the spread of terrorist propaganda. Missing from these promises, however, is an acknowledgement that proactive identification (...)
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  64. Emotional AI, Soft Biometrics and the Surveillance of Emotional Life: An Unusual Consensus on Privacy.Andrew McStay - 2020 - Big Data and Society 7 (1).
    By the early 2020s, emotional artificial intelligence will become increasingly present in everyday objects and practices such as assistants, cars, games, mobile phones, wearables, toys, marketing, insurance, policing, education and border controls. There is also keen interest in using these technologies to regulate and optimize the emotional experiences of spaces, such as workplaces, hospitals, prisons, classrooms, travel infrastructures, restaurants, retail and chain stores. Developers frequently claim that their applications do not identify people. Taking the claim at face value, this paper (...)
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  65.  5
    Personal Choices and Situated Data: Privacy Negotiations and the Acceptance of Household Intelligent Personal Assistants.Anouk Mols & Jason Pridmore - 2020 - Big Data and Society 7 (1).
    The emergence of personal assistants in the form of smart speakers has begun to significantly alter people’s everyday experiences with technology. The rate at which household Intelligent Personal Assistants such as Amazon’s Echo and Google Home emerged in household spaces has been rapid. They have begun to move human–computer interaction from text-based to voice-activated input, offering a multiplicity of features through speech. The supporting infrastructure connects with artificial intelligence and the internet of things, allowing digital interfaces with domestic appliances, lighting (...)
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  66.  8
    How to Translate Artificial Intelligence? Myths and Justifications in Public Discourse.Kevin Morin, Marius Senneville & Jonathan Roberge - 2020 - Big Data and Society 7 (1).
    Automated technologies populating today’s online world rely on social expectations about how “smart” they appear to be. Algorithmic processing, as well as bias and missteps in the course of their development, all come to shape a cultural realm that in turn determines what they come to be about. It is our contention that a robust analytical frame could be derived from culturally driven Science and Technology Studies while focusing on Callon’s concept of translation. Excitement and apprehensions must find a specific (...)
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  67. Sunlight Alone is Not a Disinfectant: Consent and the Futility of Opening Big Data Black Boxes.Jonathan A. Obar - 2020 - Big Data and Society 7 (1).
    In our attempts to achieve privacy and reputation deliverables, advocating for service providers and other data managers to open Big Data black boxes and be more transparent about consent processes, algorithmic details, and data practice is easy. Moving from this call to meaningful forms of transparency, where the Big Data details are available, useful, and manageable is more difficult. Most challenging is moving from that difficult task of meaningful transparency to the seemingly impossible scenario of achieving, consistently and ubiquitously, meaningful (...)
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  68. Beyond Algorithmic Reformism: Forward Engineering the Designs of Algorithmic Systems.Peter Polack - 2020 - Big Data and Society 7 (1).
    This article develops a method for investigating the consequences of algorithmic systems according to the documents that specify their design constrains. As opposed to reverse engineering algorithms to identify how their logic operates, the article proposes to design or "forward engineer" algorithmic systems in order to theorize how their consequences are informed by design constraints: the specific problems, use cases, and presuppositions that they respond to. This demands a departure from algorithmic reformism, which responds to concerns about the consequences of (...)
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  69. Of ‘Black Boxes’ and Algorithmic Decision-Making in (Higher) Education – A Commentary.Paul Prinsloo - 2020 - Big Data and Society 7 (1).
    Higher education institutions have access to higher volumes and a greater variety and granularity of student data, often in real-time, than ever before. As such, the collection, analysis and use of student data are increasingly crucial in operational and strategic planning, and in delivering appropriate and effective learning experiences to students. Student data – not only in what data is collected, but also how the data is framed and used – has material and discursive effects, both permanent and fleeting. We (...)
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  70. Data for Queer Lives: How LGBTQ Gender and Sexuality Identities Challenge Norms of Demographics.Spencer Ruelos & Bonnie Ruberg - 2020 - Big Data and Society 7 (1).
    In this article, we argue that dominant norms of demographic data are insufficient for accounting for the complexities that characterize many lesbian, gay, bisexual, transgender, and queer lives. Here, we draw from the responses of 178 people who identified as non-heterosexual or non-cisgender to demographic questions we developed regarding gender and sexual orientation. Demographic data commonly imagines identity as fixed, singular, and discrete. However, our findings suggest that, for LGBTQ people, gender and sexual identities are often multiple and in flux. (...)
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  71.  4
    Folk Theories of Algorithmic Recommendations on Spotify: Enacting Data Assemblages in the Global South.Mónica Sancho, Ricardo Solís, Andrés Segura-Castillo & Ignacio Siles - 2020 - Big Data and Society 7 (1).
    This paper examines folk theories of algorithmic recommendations on Spotify in order to make visible the cultural specificities of data assemblages in the global South. The study was conducted in Costa Rica and draws on triangulated data from 30 interviews, 4 focus groups with 22 users, and the study of “rich pictures” made by individuals to graphically represent their understanding of algorithmic recommendations. We found two main folk theories: one that personifies Spotify and another one that envisions it as a (...)
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  72.  5
    Prospecting (in) the Data Sciences.Stephen C. Slota, Andrew S. Hoffman, David Ribes & Geoffrey C. Bowker - 2020 - Big Data and Society 7 (1).
    Data science is characterized by engaging heterogeneous data to tackle real world questions and problems. But data science has no data of its own and must seek it within real world domains. We call this search for data “prospecting” and argue that the dynamics of prospecting are pervasive in, even characteristic of, data science. Prospecting aims to render the data, knowledge, expertise, and practices of worldly domains available and tractable to data science method and epistemology. Prospecting precedes data synthesis, analysis, (...)
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  73. Evolving Data Teams: Tensions Between Organisational Structure and Professional Subculture.Florian Stalph - 2020 - Big Data and Society 7 (1).
    This study explores the integration of data journalism within three European legacy news organisations through the lens of organisational structure and professional culture. Interviews with data journalists and editors suggest that professional routines resonate with established data journalism epistemologies, values, and norms that appear to be constitutional for an inter-organisational data journalism subculture. At the same time, organisational structure either integrates the journalistic subculture by increasing levels of complexity, formalisation, and centralisation or rejects it by not accommodating it structurally or (...)
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  74. A Dialogic Analysis of Hello Barbie’s Conversations with Children.Valerie Steeves - 2020 - Big Data and Society 7 (1).
    This paper analyses Hello Barbie as a commercial artefact to explore how big data practices are reshaping the enterprise of marketing. The doll uses voice recognition software to ‘listen’ to the child and ‘talk back’ by algorithmically selecting a response from 8000 predetermined lines of dialogue. As such, it is a useful example of how marketers use customer relationship management systems that rely on sophisticated data collection and analysis techniques to create a relationship between companies and customers in which both (...)
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  75.  2
    Playing with Machines: Using Machine Learning to Understand Automated Copyright Enforcement at Scale.Nicolas P. Suzor & Joanne E. Gray - 2020 - Big Data and Society 7 (1).
    This article presents the results of methodological experimentation that utilises machine learning to investigate automated copyright enforcement on YouTube. Using a dataset of 76.7 million YouTube videos, we explore how digital and computational methods can be leveraged to better understand content moderation and copyright enforcement at a large scale.We used the BERT language model to train a machine learning classifier to identify videos in categories that reflect ongoing controversies in copyright takedowns. We use this to explore, in a granular way, (...)
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  76.  1
    The Nordic Data Imaginary.Heta Tarkkala, Karoliina Snell & Aaro Tupasela - 2020 - Big Data and Society 7 (1).
    The Nordic countries aim to have a unique place within the European and global health data economy. They have extensive nationally maintained and centralized health data records, as well as numerous biobanks where data from individuals can be connected based on personal identification numbers. Much of this phenomenon can be attributed to the emergence and development of the Nordic welfare state, where Nordic countries sought to systematically collect large amounts of population data to guide decision making and improve the health (...)
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  77.  1
    Doing Nothing Does Something: Embodiment and Data in the COVID-19 Pandemic.Mickey Vallee - 2020 - Big Data and Society 7 (1).
    The COVID-19 pandemic redefines how we think about the body, physiologically and socially. But what does it mean to have and to be a body in the COVID-19 pandemic? The COVID-19 pandemic offers data scholars the unique opportunity, and perhaps obligation, to revisit and reinvent the fundamental concepts of our mediated experiences. The article critiques the data double, a longstanding concept in critical data and media studies, as incompatible with the current public health and social distancing imperative. The data double, (...)
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  78.  3
    Establishing a Social Licence for Financial Technology: Reflections on the Role of the Private Sector in Pursuing Ethical Data Practices.Aad van Moorsel, Karen Elliott, Kovila Coopamootoo, Peter Carmichael, Ehsan Toreini & Mhairi Aitken - 2020 - Big Data and Society 7 (1).
    Current attention directed at ethical dimensions of data and Artificial Intelligence have led to increasing recognition of the need to secure and maintain public support for uses of people’s data. This is essential to establish a “Social Licence” for current and future practices. The notion of a “Social Licence” recognises that there can be meaningful differences between what is legally permissible and what is socially acceptable. Establishing a Social Licence entails public engagement to build relationships of trust and ensure that (...)
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  79.  6
    The Value of Big Data in Government: The Case of ‘Smart Cities’.C. William R. Webster & Karl Löfgren - 2020 - Big Data and Society 7 (1).
    The emergence of Big Data has added a new aspect to conceptualizing the use of digital technologies in the delivery of public services and for realizing digital governance. This article explores, via the ‘value-chain’ approach, the evolution of digital governance research, and aligns it with current developments associated with data analytics, often referred to as ‘Big Data’. In many ways, the current discourse around Big Data reiterates and repeats established commentaries within the eGovernment research community. This body of knowledge provides (...)
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  80.  3
    Big Data and Surveillance: Hype, Commercial Logics and New Intimate Spheres.William Webster & Kirstie Ball - 2020 - Big Data and Society 7 (1).
    Big Data Analytics promises to help companies and public sector service providers anticipate consumer and service user behaviours so that they can be targeted in greater depth. The attempts made by these organisations to connect analytically with users raise questions about whether surveillance, and its associated ethical and rights-based concerns, are intensified. The articles in this special themed issue explore this question from both organisational and user perspectives. They highlight the hype which firms use to drive consumer, employee and service (...)
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  81.  1
    “We Called That a Behavior”: The Making of Institutional Data.Madisson Whitman - 2020 - Big Data and Society 7 (1).
    Predictive uses of data are becoming widespread in institutional settings as actors seek to anticipate people and their activities. Predictive modeling is increasingly the subject of scholarly and public criticism. Less common, however, is scrutiny directed at the data that inform predictive models beyond concerns about homogenous training data or general epistemological critiques of data. In this paper, I draw from a qualitative case study set in higher education in the United States to investigate the making of data. Data analytics (...)
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  82. Relational Data Paradigms: What Do We Learn by Taking the Materiality of Databases Seriously?Karen M. Wickett & Andrea K. Thomer - 2020 - Big Data and Society 7 (1).
    Although databases have been well-defined and thoroughly discussed in the computer science literature, the actual users of databases often have varying definitions and expectations of this essential computational infrastructure. Systems administrators and computer science textbooks may expect databases to be instantiated in a small number of technologies, but there are numerous examples of databases in non-conventional or unexpected technologies, such as spreadsheets or other assemblages of files linked through code. Consequently, we ask: How do the materialities of non-conventional databases differ (...)
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  83.  2
    Everyday Curation? Attending to Data, Records and Record Keeping in the Practices of Self-Monitoring.Rosalind Williams, Flis Henwood, Catherine Will & Kate Weiner - 2020 - Big Data and Society 7 (1).
    This paper is concerned with everyday data practices, considering how people record data produced through self-monitoring. The analysis unpacks the relationships between taking a measure, and making and reviewing records. The paper is based on an interview study with people who monitor their blood pressure and/or body mass index/weight. Animated by discussions of ‘data power’ which are, in part, predicated on the flow and aggregation of data, we aim to extend important work concerning the everyday constitution of digital data. In (...)
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  84.  4
    Manipulate to Empower: Hyper-Relevance and the Contradictions of Marketing in the Age of Surveillance Capitalism.Detlev Zwick & Aron Darmody - 2020 - Big Data and Society 7 (1).
    In this article, we explore how digital marketers think about marketing in the age of Big Data surveillance, automatic computational analyses, and algorithmic shaping of choice contexts. Our starting point is a contradiction at the heart of digital marketing namely that digital marketing brings about unprecedented levels of consumer empowerment and autonomy and total control over and manipulation of consumer decision-making. We argue that this contradiction of digital marketing is resolved via the notion of relevance, which represents what Fredric Jameson (...)
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