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  1.  2
    Big Data in the Workplace: Privacy Due Diligence as a Human Rights-Based Approach to Employee Privacy Protection.Jeremias Adams-Prassl, Isabelle Wildhaber & Isabel Ebert - 2021 - Big Data and Society 8 (1).
    Data-driven technologies have come to pervade almost every aspect of business life, extending to employee monitoring and algorithmic management. How can employee privacy be protected in the age of datafication? This article surveys the potential and shortcomings of a number of legal and technical solutions to show the advantages of human rights-based approaches in addressing corporate responsibility to respect privacy and strengthen human agency. Based on this notion, we develop a process-oriented model of Privacy Due Diligence to complement existing frameworks (...)
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  2.  2
    Mapping Persian Twitter: Networks and Mechanism of Political Communication in Iranian 2017 Presidential Election.Marzieh Adham & Hossein Kermani - 2021 - Big Data and Society 8 (1).
    This paper investigates the structure of networked publics and their sharing practices in Persian Twitter during a period surrounding Iran’s 2017 presidential election. Building on networked gatekeeping and framing theories, we used a mixed methodological approach to analyze a dataset of 2,596,284 Persian tweets. Results revealed that Twitter provided a space for Iranians to discuss public topics. However, this space is not necessarily used by voiceless and marginalized groups; and the uses are not limited to discussing controversial issues. The growing (...)
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  3.  2
    Encrypting Human Rights: The Intertwining of Resistant Voices in the UK State Surveillance Debate.James Allen-Robertson & Amy Stevens - 2021 - Big Data and Society 8 (1).
    The Snowden revelations in 2013 redrew the lines of debate surrounding surveillance, exposing the extent of state surveillance across multiple nations and triggering legislative reform in many. In the UK, this was in the form of the Investigatory Powers Act. As a contribution to understanding resistance to expanding state surveillance activities, this article reveals the intertwining of diverse interests and voices which speak in opposition to UK state surveillance. Through a computational topic modelling-based mixed methods analysis of the submissions made (...)
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  4.  2
    Racial Formation, Coloniality, and Climate Finance Organizations: Implications for Emergent Data Projects in the Pacific.Kirsty Anantharajah - 2021 - Big Data and Society 8 (1).
    This commentary explores the potential consequence of latent racial formation in emergent climate finance data projects and draws from ethnographic research on climate finance governance conducted in Fiji. Climate finance data projects emerging in the Pacific aim to ease the flow of finance from the Global North to the South. These emergent data projects, such as renewable energy resource availability and investment mapping, are imbedded in the climate finance organizations that fund, develop, and use them. Thus, the commentary explores climate (...)
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  5.  3
    Big Data for Climate Action or Climate Action for Big Data?Melissa Aronczyk & Maria I. Espinoza - 2021 - Big Data and Society 8 (1).
    Under the banner of “data for good,” companies in the technology, finance, and retail sectors supply their proprietary datasets to development agencies, NGOs, and intergovernmental organizations to help solve an array of social problems. We focus on the activities and implications of the Data for Climate Action campaign, a set of public–private collaborations that wield user data to design innovative responses to the global climate crisis. Drawing on in-depth interviews, first-hand observations at “data for good” events, intergovernmental and international organizational (...)
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  6.  1
    Machine Learning in Tutorials – Universal Applicability, Underinformed Application, and Other Misconceptions.Andreas Breiter, Juliane Jarke & Hendrik Heuer - 2021 - Big Data and Society 8 (1).
    Machine learning has become a key component of contemporary information systems. Unlike prior information systems explicitly programmed in formal languages, ML systems infer rules from data. This paper shows what this difference means for the critical analysis of socio-technical systems based on machine learning. To provide a foundation for future critical analysis of machine learning-based systems, we engage with how the term is framed and constructed in self-education resources. For this, we analyze machine learning tutorials, an important information source for (...)
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  7.  11
    Studying the COVID-19 Infodemic at Scale.Sylvie Briand, Pier Luigi Sacco, Manlio De Domenico & Anatoliy Gruzd - 2021 - Big Data and Society 8 (1).
    This special theme issue of Big Data & Society presents leading-edge, interdisciplinary research that focuses on examining how health-related information is circulating on social media. In particular, we are focusing on how computational and Big Data approaches can help to provide a better understanding of the ongoing COVID-19 infodemic and to develop effective strategies to combat it.
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  8.  1
    Digital Failure: Unbecoming the “Good” Data Subject Through Entropic, Fugitive, and Queer Data.Lauren E. Bridges - 2021 - Big Data and Society 8 (1).
    This paper explores the political potential of digital failure as a refusal to work in service of today’s dataveillance society. Moving beyond criticisms of flawed digital systems, this paper traces the moments of digital failure that seek to break, rather than fix, existing systems. Instead, digital failure is characterized by pesky data that sneaks through the cracks of digital capitalism and dissipates into the unproductive; it supports run-away data prone to misidentifications by digital marketers, coders, and content moderators; and it (...)
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  9.  1
    Against Carceral Data Collection in Response to Anti-Asian Violences.Matthew Bui & Rachel Kuo - 2021 - Big Data and Society 8 (1).
    This commentary reflects on recent instances of anti-Asian violence and state responses to redress violence through data-driven strategies. Data collection often presents itself as an appealing strategy, due to impacted communities’ desires for evidence and metrics to substantiate political claims. Yet, data collection can bolster the carceral state. This commentary takes an antagonistic approach to policing, including the ongoing creation of data infrastructures by—and for—law enforcement through hate crimes legislation. We critically discuss the challenges and possibilities in building towards anti-carceral (...)
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  10.  1
    Archival Strategies for Contemporary Collecting in a World of Big Data: Challenges and Opportunities with Curating the UK Web Archive.Helena Byrne & Nicola Jayne Bingham - 2021 - Big Data and Society 8 (1).
    In this contribution, we will discuss the opportunities and challenges arising from memory institutions' need to redefine their archival strategies for contemporary collecting in a world of big data. We will reflect on this topic by critically examining the case study of the UK Web Archive, which is made up of the six UK Legal Deposit Libraries: the British Library, National Library of Scotland, National Library of Wales, Bodleian Libraries Oxford, Cambridge University Library and Trinity College Dublin. The UK Web (...)
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  11.  5
    COVID-19, Digital Health Technology and the Politics of the Unprecedented.Benjamin Chin-Yee & Dillon Wamsley - 2021 - Big Data and Society 8 (1).
    The COVID-19 global pandemic has stretched the capacities of public health institutions and health systems around the world, opening the door to a range of technologically-driven solutions. In this article, we seek to historicize the expanding role of digital health technologies and examine the political-economic context from which they have emerged. Drawing on critical insights from science and technology studies, we maintain that the rise of digital health technologies has been catalyzed by broad shifts in global health governance that have (...)
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  12. The Cancer Multiple: Producing and Translating Genomic Big Data Into Oncology Care.Peter A. Chow-White & Tiên-Dung Hà - 2021 - Big Data and Society 8 (1).
    This article provides an ethnographic account of how Big Data biology is produced, interpreted, debated, and translated in a Big Data-driven cancer clinical trial, entitled “Personalized OncoGenomics,” in Vancouver, Canada. We delve into epistemological differences between clinical judgment, pathological assessment, and bioinformatic analysis of cancer. To unpack these epistemological differences, we analyze a set of gazes required to produce Big Data biology in cancer care: clinical gaze, molecular gaze, and informational gaze. We are concerned with the interactions of these bodily (...)
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  13.  13
    Data Sovereignty: A Review.Peter Dabrock, Max Tretter, Matthias Braun & Patrik Hummel - 2021 - Big Data and Society 8 (1).
    New data-driven technologies yield benefits and potentials, but also confront different agents and stakeholders with challenges in retaining control over their data. Our goal in this study is to arrive at a clear picture of what is meant by data sovereignty in such problem settings. To this end, we review 341 publications and analyze the frequency of different notions such as data sovereignty, digital sovereignty, and cyber sovereignty. We go on to map agents they concern, in which context they appear, (...)
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  14. The Algorithm Audit: Scoring the Algorithms That Score Us.Jovana Davidovic, Shea Brown & Ali Hasan - 2021 - Big Data and Society 8 (1).
    In recent years, the ethical impact of AI has been increasingly scrutinized, with public scandals emerging over biased outcomes, lack of transparency, and the misuse of data. This has led to a growing mistrust of AI and increased calls for mandated ethical audits of algorithms. Current proposals for ethical assessment of algorithms are either too high level to be put into practice without further guidance, or they focus on very specific and technical notions of fairness or transparency that do not (...)
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  15. Data Diaries: A Situated Approach to the Study of Data.Giovanni Dolif Neto, Flávio Horita, João Porto de Albuquerque, Mário Henrique da Mata Martins & Nathaniel Tkacz - 2021 - Big Data and Society 8 (1).
    This article adapts the ethnographic medium of the diary to develop a method for studying data and related data practices. The article focuses on the creation of one data diary, developed iteratively over three years in the context of a national centre for monitoring disasters and natural hazards in Brazil. We describe four points of focus involved in the creation of a data diary – spaces, interfaces, types and situations – before reflecting on the value of this method. We suggest (...)
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  16.  1
    Making Sense of Algorithms: Relational Perception of Contact Tracing and Risk Assessment During COVID-19.Ross Graham & Chuncheng Liu - 2021 - Big Data and Society 8 (1).
    Governments and citizens of nearly every nation have been compelled to respond to COVID-19. Many measures have been adopted, including contact tracing and risk assessment algorithms, whereby citizen whereabouts are monitored to trace contact with other infectious individuals in order to generate a risk status via algorithmic evaluation. Based on 38 in-depth interviews, we investigate how people make sense of Health Code, the Chinese contact tracing and risk assessment algorithmic sociotechnical assemblage. We probe how people accept or resist Health Code (...)
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  17.  1
    Epistemologies of Predictive Policing: Mathematical Social Science, Social Physics and Machine Learning.Jens Hälterlein - 2021 - Big Data and Society 8 (1).
    Predictive policing has become a new panacea for crime prevention. However, we still know too little about the performance of computational methods in the context of predictive policing. The paper provides a detailed analysis of existing approaches to algorithmic crime forecasting. First, it is explained how predictive policing makes use of predictive models to generate crime forecasts. Afterwards, three epistemologies of predictive policing are distinguished: mathematical social science, social physics and machine learning. Finally, it is shown that these epistemologies have (...)
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  18.  1
    Analysing Discourse Around COVID-19 in the Australian Twittersphere: A Real-Time Corpus-Based Analysis.Sam Hames, Michael Haugh & Martin Schweinberger - 2021 - Big Data and Society 8 (1).
    Public discourse about the COVID-19 that appears on Twitter and other social media platforms provides useful insights into public concerns and responses to the pandemic. However, acknowledging that public discourse around COVID-19 is multi-faceted and evolves over time poses both analytical and ontological challenges. Studies that use text-mining approaches to analyse responses to major events commonly treat public discourse on social media as an undifferentiated whole, without systematically examining the extent to which that discourse consists of distinct sub-discourses or which (...)
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  19.  1
    How Partners Mediate Platform Power: Mapping Business and Data Partnerships in the Social Media Ecosystem.Anne Helmond & Fernando N. van der Vlist - 2021 - Big Data and Society 8 (1).
    Social media platforms’ digital advertising revenues depend considerably on partnerships. Business partnerships are endemic and essential to the business of platforms, yet their role remains relatively underexplored in the literature on platformisation and platform power. This article considers the significance of partnerships in the social media ecosystem to better understand how industry platforms, and the infrastructure they build, mediate and shape platform power and governance. We argue that partners contribute to ‘platformisation’ through their collective development of business-to-business platform infrastructures. Specifically, (...)
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  20.  1
    Turning Biases Into Hypotheses Through Method: A Logic of Scientific Discovery for Machine Learning.Maja Bak Herrie & Simon Aagaard Enni - 2021 - Big Data and Society 8 (1).
    Machine learning systems have shown great potential for performing or supporting inferential reasoning through analyzing large data sets, thereby potentially facilitating more informed decision-making. However, a hindrance to such use of ML systems is that the predictive models created through ML are often complex, opaque, and poorly understood, even if the programs “learning” the models are simple, transparent, and well understood. ML models become difficult to trust, since lay-people, specialists, and even researchers have difficulties gauging the reasonableness, correctness, and reliability (...)
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  21. Assessing Biases, Relaxing Moralism: On Ground-Truthing Practices in Machine Learning Design and Application.Florian Jaton - 2021 - Big Data and Society 8 (1).
    This theoretical paper considers the morality of machine learning algorithms and systems in the light of the biases that ground their correctness. It begins by presenting biases not as a priori negative entities but as contingent external referents—often gathered in benchmarked repositories called ground-truth datasets—that define what needs to be learned and allow for performance measures. I then argue that ground-truth datasets and their concomitant practices—that fundamentally involve establishing biases to enable learning procedures—can be described by their respective morality, here (...)
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  22. What Do We See When We Look at Networks: Visual Network Analysis, Relational Ambiguity, and Force-Directed Layouts.Pablo Jensen, Mathieu Jacomy & Tommaso Venturini - 2021 - Big Data and Society 8 (1).
    It is increasingly common in natural and social sciences to rely on network visualizations to explore relational datasets and illustrate findings. Such practices have been around long enough to prove that scholars find it useful to project networks in a two-dimensional space and to use their visual qualities as proxies for their topological features. Yet these practices remain based on intuition, and the foundations and limits of this type of exploration are still implicit. To fill this lack of formalization, this (...)
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  23.  1
    Algorithms as Organizational Figuration: The Sociotechnical Arrangements of a Fintech Start-Up.Sine N. Just, Ib T. Gulbrandsen & Sara Dahlman - 2021 - Big Data and Society 8 (1).
    Building on critical approaches that understand algorithms in terms of communication, culture and organization, this paper offers the supplementary conceptualization of algorithms as organizational figuration, defined as material and meaningful sociotechnical arrangements that develop in spatiotemporal processes and are shaped by multiple enactments of affordance–agency relations. We develop this conceptualization through a case study of a Danish fintech start-up that uses machine learning to create opportunities for sustainable pensions investments. By way of ethnographic and literary methodology, we provide an in-depth (...)
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  24.  3
    Blockchain Imperialism in the Pacific.Olivier Jutel - 2021 - Big Data and Society 8 (1).
    The rise of blockchain as a techno-solution in the development sector underscores the critical imbalances of data power under ‘computational capitalism’. This article will consider the political economy of techno-solutionist and blockchain discourses in the developing world, using as its object of study blockchain projects in Pacific Island nations. Backed by US State Department soft power initiatives such as Tech Camp, these projects inculcate tech-driven notions of economic and political development, or ICT4D, while opening up new terrains for data accumulation (...)
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  25.  1
    ‘It Depends on Your Threat Model’: The Anticipatory Dimensions of Resistance to Data-Driven Surveillance.Becky Kazansky - 2021 - Big Data and Society 8 (1).
    While many forms of data-driven surveillance are now a ‘fact’ of contemporary life amidst datafication, obtaining concrete knowledge of how different institutions exploit data presents an ongoing challenge, requiring the expertise and power to untangle increasingly complex and opaque technological and institutional arrangements. The how and why of potential surveillance are thus wrapped in a form of continuously produced uncertainty. How then, do affected groups and individuals determine how to counter the threats and harms of surveillance? Responding to an interdisciplinary (...)
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  26.  2
    Heritage-Based Tribalism in Big Data Ecologies: Deploying Origin Myths for Antagonistic Othering.Marta Krzyzanska & Chiara Bonacchi - 2021 - Big Data and Society 8 (1).
    This article presents a conceptual and methodological framework to study heritage-based tribalism in Big Data ecologies by combining approaches from the humanities, social and computing sciences. We use such a framework to examine how ideas of human origin and ancestry are deployed on Twitter for purposes of antagonistic ‘othering’. Our goal is to equip researchers with theory and analytical tools for investigating divisive online uses of the past in today’s networked societies. In particular, we apply notions of heritage, othering and (...)
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  27. “More Like a Support Tool”: Ambivalences Around Digital Health From Medical Developers’ Perspective.Sarah Lenz - 2021 - Big Data and Society 8 (1).
    Against the background of the increasing importance of digitization in health care, the paper examines how medical practitioners who are involved in the development of digital health technologies legitimate and criticize the implementation and use of digital health technologies. Adopting an institutional logics perspective, the study is based on qualitative interviews with persons working at the interface of medicine and digital technologies development in Switzerland. The findings indicate that the developers believe that digital health technologies could harmonize current conflicts between (...)
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  28.  3
    Knowledge Co-Creation in Participatory Policy and Practice: Building Community Through Data-Driven Direct Democracy.Siaw-Teng Liaw, Patty Kostkova, Andreea Molnar, Timothy Kariotis, Ann Borda & Myron A. Godinho - 2021 - Big Data and Society 8 (1).
    Engaging citizens with digital technology to co-create data, information and knowledge has widely become an important strategy for informing the policy response to COVID-19 and the ‘infodemic’ of misinformation in cyberspace. This move towards digital citizen participation aligns well with the United Nations’ agenda to encourage the use of digital tools to enable data-driven, direct democracy. From data capture to information generation, and knowledge co-creation, every stage of the data lifecycle bears important considerations to inform policy and practice. Drawing on (...)
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  29.  1
    Towards Psychological Herd Immunity: Cross-Cultural Evidence for Two Prebunking Interventions Against COVID-19 Misinformation.Sander van der Linden, William P. McClanahan, Fatih Uenal, Manon Berriche, Jon Roozenbeek & Melisa Basol - 2021 - Big Data and Society 8 (1).
    Misinformation about the novel coronavirus is a pressing societal challenge. Across two studies, one preregistered, we assess the efficacy of two ‘prebunking’ interventions aimed at improving people’s ability to spot manipulation techniques commonly used in COVID-19 misinformation across three different languages. We find that Go Viral!, a novel five-minute browser game, increases the perceived manipulativeness of misinformation about COVID-19, improves people’s attitudinal certainty in their ability to spot misinformation and reduces self-reported willingness to share misinformation with others. The first two (...)
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  30. Dashboard Stories: How Narratives Told by Predictive Analytics Reconfigure Roles, Risk and Sociality in Education.Felicitas Macgilchrist & Juliane Jarke - 2021 - Big Data and Society 8 (1).
    In this paper, we explore how the development and affordances of predictive analytics may impact how teachers and other educational actors think about and teach students and, more broadly, how society understands education. Our particular focus is on the data dashboards of learning support systems which are based on Machine Learning. While previous research has focused on how these systems produce credible knowledge, we explore here how they also produce compelling, persuasive and convincing narratives. Our main argument is that particular (...)
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  31.  2
    Identifying and Characterizing Scientific Authority-Related Misinformation Discourse About Hydroxychloroquine on Twitter Using Unsupervised Machine Learning.Tim K. Mackey, Jiawei Li & Michael Robert Haupt - 2021 - Big Data and Society 8 (1).
    This study investigates the types of misinformation spread on Twitter that evokes scientific authority or evidence when making false claims about the antimalarial drug hydroxychloroquine as a treatment for COVID-19. Specifically, we examined tweets generated after former U.S. President Donald Trump retweeted misinformation about the drug using an unsupervised machine learning approach called the biterm topic model that is used to cluster tweets into misinformation topics based on textual similarity. The top 10 tweets from each topic cluster were content coded (...)
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  32. Knowing When to Act: A Call for an Open Misinformation Library to Guide Actionable Surveillance.Kenneth D. Mandl, Amalie Dyda, Maryke Steffens & Adam G. Dunn - 2021 - Big Data and Society 8 (1).
    The design and reporting of data-driven studies seeking to measure misinformation are patchy and inconsistent, and these studies rarely measure associations with, or effects on, behaviour. The consequence is that data-driven misinformation studies are not yet useful as an empirical basis for guiding when to act on emerging misinformation threats, or for deciding when it is more appropriate to do nothing to avoid inadvertently amplifying misinformation. In a narrative review focused on examples of health-related misinformation, we take a critical perspective (...)
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  33.  6
    The COVID-19 Infodemic: Twitter Versus Facebook.Filippo Menczer, John Bryden, Christopher Torres-Lugo, David Axelrod, Pik-Mai Hui, Francesco Pierri & Kai-Cheng Yang - 2021 - Big Data and Society 8 (1).
    The global spread of the novel coronavirus is affected by the spread of related misinformation—the so-called COVID-19 Infodemic—that makes populations more vulnerable to the disease through resistance to mitigation efforts. Here, we analyze the prevalence and diffusion of links to low-credibility content about the pandemic across two major social media platforms, Twitter and Facebook. We characterize cross-platform similarities and differences in popular sources, diffusion patterns, influencers, coordination, and automation. Comparing the two platforms, we find divergence among the prevalence of popular (...)
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  34. Communicating Public Health During COVID-19, Implications for Vaccine Rollout.Annemarie Naylor, Maeve Walsh, Josefine Magnusson & Peter S. Bloomfield - 2021 - Big Data and Society 8 (1).
    A large body of information and opinion related to COVID-19 is being shared via social media platforms. Recent reports have raised concerns about the reliability and verifiability of said information being disseminated and the way systems, processes and design of the platforms facilitates such spread. This, alongside other areas of concern, has resulted in several social media platforms taking steps towards tackling the spread of mis- and dis-information. Here we discuss approaches to online public health messaging from a range of (...)
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  35. Lifting the Curtain: Strategic Visibility of Human Labour in AI-as-a-Service.Gemma Newlands - 2021 - Big Data and Society 8 (1).
    Artificial Intelligence-as-a-Service empowers individuals and organisations to access AI on-demand, in either tailored or ‘off-the-shelf’ forms. However, institutional separation between development, training and deployment can lead to critical opacities, such as obscuring the level of human effort necessary to produce and train AI services. Information about how, where, and for whom AI services have been produced are valuable secrets, which vendors strategically disclose to clients depending on commercial interests. This article provides a critical analysis of how AIaaS vendors manipulate the (...)
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  36.  1
    The Data Archive as Factory: Alienation and Resistance of Data Processors.Jean-Christophe Plantin - 2021 - Big Data and Society 8 (1).
    Archival data processing consists of cleaning and formatting data between the moment a dataset is deposited and its publication on the archive’s website. In this article, I approach data processing by combining scholarship on invisible labor in knowledge infrastructures with a Marxian framework and show the relevance of considering data processing as factory labor. Using this perspective to analyze ethnographic data collected during a six-month participatory observation at a U.S. data archive, I generate a taxonomy of the forms of alienation (...)
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  37.  7
    Toxicity and Verbal Aggression on Social Media: Polarized Discourse on Wearing Face Masks During the COVID-19 Pandemic.Rajiv N. Rimal, Daniel J. Barnett, Neil Alperstein & Paola Pascual-Ferrá - 2021 - Big Data and Society 8 (1).
    Medical and public health professionals recommend wearing face masks to combat the spread of the coronavirus disease of 2019. While the majority of people in the United States support wearing face masks as an effective tool to combat COVID-19, a smaller percentage declared the recommendation by public health agencies as a government imposition and an infringement on personal liberty. Social media play a significant role in amplifying public health issues, whereby a minority against the imposition can speak loudly, perhaps using (...)
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  38.  2
    Visual Media Analysis for Instagram and Other Online Platforms.Richard Rogers - 2021 - Big Data and Society 8 (1).
    Instagram is currently the social media platform most associated with online images, but images from other platforms also can be collected and grouped, arrayed by similarity, stacked, matched, stained, labelled, depicted as network, placed side by side and otherwise analytically displayed. In the following, the initial focus is on Instagram, together with certain schools of thought such as Instagramism and Instagrammatics for its aesthetic and visual cultural study. Building on those two approaches, it subsequently focuses on other web and social (...)
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  39.  1
    Emotional Artificial Intelligence in Children’s Toys and Devices: Ethics, Governance and Practical Remedies.Gilad Rosner & Andrew McStay - 2021 - Big Data and Society 8 (1).
    This article examines the social acceptability and governance of emotional artificial intelligence in children’s toys and other child-oriented devices. To explore this, it conducts interviews with stakeholders with a professional interest in emotional AI, toys, children and policy to consider implications of the usage of emotional AI in children’s toys and services. It also conducts a demographically representative UK national survey to ascertain parental perspectives on networked toys that utilise data about emotions. The article highlights disquiet about the evolution of (...)
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  40.  4
    Between Surveillance and Recognition: Rethinking Digital Identity in Aid.Emrys Schoemaker, Aaron Martin, Margie Cheesman & Keren Weitzberg - 2021 - Big Data and Society 8 (1).
    Identification technologies like biometrics have long been associated with securitisation, coercion and surveillance but have also, in recent years, become constitutive of a politics of empowerment, particularly in contexts of international aid. Aid organisations tend to see digital identification technologies as tools of recognition and inclusion rather than oppressive forms of monitoring, tracking and top-down control. In addition, practices that many critical scholars describe as aiding surveillance are often experienced differently by humanitarian subjects. This commentary examines the fraught questions this (...)
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  41. Identifying How COVID-19-Related Misinformation Reacts to the Announcement of the UK National Lockdown: An Interrupted Time-Series Study.Sally Sheard, Roberto Vivancos, Alex Singleton, Henrdramoorthy Maheswaran, Emily Dearden, Andrew Davies, John Tulloch, Patricia Rossini, Andrew Morse, Chris Kypridemos, Frances Darlington Pollock, Darren Charles, Francisco Rowe, Elena Musi & Mark Green - 2021 - Big Data and Society 8 (1).
    COVID-19 is unique in that it is the first global pandemic occurring amidst a crowded information environment that has facilitated the proliferation of misinformation on social media. Dangerous misleading narratives have the potential to disrupt ‘official’ information sharing at major government announcements. Using an interrupted time-series design, we test the impact of the announcement of the first UK lockdown on short-term trends of misinformation on Twitter. We utilise a novel dataset of all COVID-19-related social media posts on Twitter from the (...)
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  42. World Heritage Sites on Wikipedia: Cultural Heritage Activism in a Context of Constrained Agency.Prema Smith & Ben Marwick - 2021 - Big Data and Society 8 (1).
    UNESCO World Heritage sites are places of outstanding significance and often key sources of information that influence how people interact with the past today. The process of inscription on the UNESCO list is complicated and intersects with political and commercial controversies. But how well are these controversies known to the public? Wikipedia pages on these sites offer a unique dataset for insights into public understanding of heritage controversies. The unique technicity of Wikipedia, with its bot ecosystem and editing mechanics, shapes (...)
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  43.  17
    The Case for Tracking Misinformation the Way We Track Disease.Joe Smyser, Jennifer Sittig & Erika Bonnevie - 2021 - Big Data and Society 8 (1).
    While public health organizations can detect disease spread, few can monitor and respond to real-time misinformation. Misinformation risks the public’s health, the credibility of institutions, and the safety of experts and front-line workers. Big Data, and specifically publicly available media data, can play a significant role in understanding and responding to misinformation. The Public Good Projects uses supervised machine learning to aggregate and code millions of conversations relating to vaccines and the COVID-19 pandemic broadly, in real-time. Public health researchers supervise (...)
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  44.  1
    The Value of Mass-Digitised Cultural Heritage Content in Creative Contexts.Chris Speed, Pip Thornton, Michael Smyth, Burkhard Schafer, Briana Pegado, Inge Panneels, Nicola Osborne, Susan Lechelt, Ingi Helgason, Chris Elsden, Steven Drost, Stephen Coleman & Melissa Terras - 2021 - Big Data and Society 8 (1).
    How can digitised assets of Galleries, Libraries, Archives and Museums be reused to unlock new value? What are the implications of viewing large-scale cultural heritage data as an economic resource, to build new products and services upon? Drawing upon valuation studies, we reflect on both the theory and practicalities of using mass-digitised heritage content as an economic driver, stressing the need to consider the complexity of commercial-based outcomes within the context of cultural and creative industries. However, we also problematise the (...)
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  45.  1
    One Size Does Not Fit All: Constructing Complementary Digital Reskilling Strategies Using Online Labour Market Data.Fabian Stephany - 2021 - Big Data and Society 8 (1).
    Digital technologies are radically transforming our work environments and demand for skills, with certain jobs being automated away and others demanding mastery of new digital techniques. This global challenge of rapidly changing skill requirements due to task automation overwhelms workers. The digital skill gap widens further as technological and social transformation outpaces national education systems and precise skill requirements for mastering emerging technologies, such as Artificial Intelligence, remain opaque. Online labour platforms could help us to understand this grand challenge of (...)
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  46.  12
    Countering Misinformation: A Multidisciplinary Approach.Krzysztof Suchecki, Julian Sienkiewicz, Wesley R. Moy, Janusz A. Hołyst & Kacper T. Gradoń - 2021 - Big Data and Society 8 (1).
    The article explores the concept of infodemics during the COVID-19 pandemic, focusing on the propagation of false or inaccurate information proliferating worldwide throughout the SARS-CoV-2 health crisis. We provide an overview of disinformation, misinformation and malinformation and discuss the notion of “fake news”, and highlight the threats these phenomena bear for health policies and national and international security. We discuss the mis-/disinformation as a significant challenge to the public health, intelligence, and policymaking communities and highlight the necessity to design measures (...)
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  47.  1
    “Reach the Right People”: The Politics of “Interests” in Facebook’s Classification System for Ad Targeting.Kjerstin Thorson, Chankyung Pak, Mel Medeiros & Kelley Cotter - 2021 - Big Data and Society 8 (1).
    Political campaigns increasingly rely on Facebook for reaching their constituents, particularly through ad targeting. Facebook’s business model is premised on a promise to connect advertisers with the “right” users: those likely to click, download, engage, purchase. The company pursues this promise by algorithmically inferring users’ interests from their data and providing advertisers with a means of targeting users by their inferred interests. In this study, we explore for whom this interest classification system works in order to build on conversations in (...)
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  48.  1
    Big Tech Platforms in Health Research: Re-Purposing Big Data Governance in Light of the General Data Protection Regulation’s Research Exemption.Ine Van Hoyweghen, Giuseppe Testa & Luca Marelli - 2021 - Big Data and Society 8 (1).
    The emergence of a global industry of digital health platforms operated by Big Tech corporations, and its growing entanglements with academic and pharmaceutical research networks, raise pressing questions on the capacity of current data governance models, regulatory and legal frameworks to safeguard the sustainability of the health research ecosystem. In this article, we direct our attention toward the challenges faced by the European General Data Protection Regulation in regulating the potentially disruptive engagement of Big Tech platforms in health research. The (...)
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  49.  2
    Data as Asset? The Measurement, Governance, and Valuation of Digital Personal Data by Big Tech.Callum Ward, D. T. Cochrane & Kean Birch - 2021 - Big Data and Society 8 (1).
    Digital personal data is increasingly framed as the basis of contemporary economies, representing an important new asset class. Control over these data assets seems to explain the emergence and dominance of so-called “Big Tech” firms, consisting of Apple, Microsoft, Amazon, Google/alphabet, and Facebook. These US-based firms are some of the largest in the world by market capitalization, a position that they retain despite growing policy and public condemnation—or “techlash”—of their market power based on their monopolistic control of personal data. We (...)
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  50. From FAIR Data to Fair Data Use: Methodological Data Fairness in Health-Related Social Media Research.Hywel Williams, Lora Fleming, Benedict W. Wheeler, Rebecca Lovell & Sabina Leonelli - 2021 - Big Data and Society 8 (1).
    The paper problematises the reliability and ethics of using social media data, such as sourced from Twitter or Instagram, to carry out health-related research. As in many other domains, the opportunity to mine social media for information has been hailed as transformative for research on well-being and disease. Considerations around the fairness, responsibilities and accountabilities relating to using such data have often been set aside, on the understanding that as long as data were anonymised, no real ethical or scientific issue (...)
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