Journal Description
Big Data & Society (BD&S) is an Open Access peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities and computing and their intersections with the arts and natural sciences about the implications of Big Data for societies. The Journal’s key purpose is to provide a space for connecting debates about the emerging field of Big Data practices … |
This journal is a member of the Committee on Publication Ethics (COPE).
Journal Feed
- by Monja SauvagerdBig Data & Society, Volume 11, Issue 4, October-December 2024. This paper introduces the concept of ‘oligopolistic platformisation’ to capture the specific dynamics of collaboration and competition between multinational upstream agribusinesses and Big Tech companies in the agricultural (ag) sector. We examine this phenomenon through the lens of Van Dijck et al.’s platform mechanisms: datafication, selection and commodification. Multinational agribusinesses operate sectoral ag platforms that analyse spatial, weather and agronomic data to optimise farming operations, whilst Big Tech companies provide the digital infrastructure, including cloud computing, data analytics and artificial intelligence. We explore how these pre-existing oligopolistic market structures influence […]
- by Maiju TanninenBig Data & Society, Volume 11, Issue 4, October-December 2024. Digital looping effects between algorithmic technologies and users are promoted as reshaping various industries by optimizing operations, improving predictions and creating new market opportunities. Insurers are exploring these promises by collecting customer-generated data and testing its use in risk calculations and behavioural interventions. However, these novel insurance technologies have been criticized for enabling totalizing forms of surveillance, control and discrimination, potentially leading to the foreclosure of future actions. This study tests the argument that emerging insurance technologies ‘narrow the future’ by analysing Finnish life insurers’ efforts to build a digital […]
- by Trang LeBig Data & Society, Volume 11, Issue 4, October-December 2024. From critiques of baked-in sexism in data science, to the use of data in the service of feminism, feminist data activism has emerged as a new form of feminist activism. This paper approaches feminist data activism from a data imaginary perspective, focusing on a prominent feminist initiative from Australia called She's A Crowd, an organization that claims to have crowdsourced the world's largest dataset of gendered violence. Through interviews with 11 participants who volunteered their “datafied stories” to the organization, I explore the grassroots imaginaries about what data is and […]
- by Laura LiebigBig Data & Society, Volume 11, Issue 4, October-December 2024. Artificial intelligence has become an issue in public policy. Multiple documents issued by public sector actors link artificial intelligence to a wide range of issues, problems or goals and propose corresponding measures and interventions. While there has been substantial research on national and supranational artificial intelligence strategies and regulations, this article is interested in unpacking the processes and priorities of artificial intelligence policy in the making. Conceptually, this article takes a controversy studies lens onto artificial intelligence policy, and complements this with concepts and insights from policy studies. Empirically, we […]
- by Felicitas HesselmannBig Data & Society, Volume 11, Issue 4, October-December 2024. This paper explores the dynamics of algorithmic governance, decision support systems and human involvement in the context of plagiarism screening in academic publishing. While automated plagiarism screening is widespread in editorial work, critical investigations about these decision support systems remain scarce. Focusing on the issue of human autonomy and discretion in algorithmic governance, the paper investigates the complexities of the human-in-the-loop within these screening tools. Revisiting Wanda Orlikowski's conceptual metaphor of ‘scaffolding’, the study empirically analyses interactions between editors and plagiarism screening software. It traces how these tools act as […]
- by Daniel S SchiffBig Data & Society, Volume 11, Issue 4, October-December 2024. The emerging ecosystem of artificial intelligence (AI) ethics and governance auditing has grown rapidly in recent years in anticipation of impending regulatory efforts that encourage both internal and external auditing. Yet, there is limited understanding of this evolving landscape. We conduct an interview-based study of 34 individuals in the AI ethics auditing ecosystem across seven countries to examine the motivations, key auditing activities, and challenges associated with AI ethics auditing in the private sector. We find that AI ethics audits follow financial auditing stages, but tend to lack robust stakeholder […]
- by Helen KennedyBig Data & Society, Volume 11, Issue 4, October-December 2024. As a mechanism for addressing data-related harms, fairness has been subjected to considerable criticism, seen as failing to acknowledge the power relationships that produce said harms, or as a ‘floating signifier’ devoid of specific meaning. In contrast to fairness, it is argued that equity does a better job of recognising data-related harms. Criticisms such as these emerge in specific cultural contexts and rarely acknowledge everyday understandings of terms and concepts. This paper engages with these criticisms, drawing on research exploring how 112 UK residents perceive data uses in specific public […]
- by Eduardo Paz DíazBig Data & Society, Volume 11, Issue 4, October-December 2024. The antivaccine hesitancy movement represents a challenge to public policy and platform regulations. During COVID-19, various Latin American antivaccine groups clashed with official sanitary initiatives. Despite many responses, little progress has been made in reaching these groups to transform their perceptions about the benefits of the COVID-19 vaccine. During the pandemic in Latin America, the antivaccine network Médicos por la Verdad (Doctors for the Truth) gained prominence in various countries. Finding itself limited by legal and technical restrictions, this network used alternative media such as Telegram to disseminate messages. This […]
- by Ingvild BodeBig Data & Society, Volume 11, Issue 4, October-December 2024. Stories about ‘intelligent machines’ have long featured in popular culture. Existing research has mapped these artificial intelligence (AI) narratives but lacks an in-depth understanding of (a) narratives related specifically to weaponised AI and autonomous weapon systems and (b) whether and how these narratives resonate across different states and associated cultural contexts. We speak to these gaps by examining narratives about weaponised AI across publics in France, India, Japan and the US. Based on a public opinion survey conducted in these states in 2022–2023, we find that narratives found in English-language […]
- by Gabriele de SetaBig Data & Society, Volume 11, Issue 4, October-December 2024. Advancements in generative artificial intelligence have led to a rapid proliferation of machine learning models capable of synthesizing text, images, sounds, and other kinds of content. While the increasing realism of synthetic content stokes fears about misinformation and triggers debates around intellectual property, generative models are adopted across creative industries and synthetic media seep into cultural production. Qualitative research in the social and human sciences has dedicated comparatively little attention to this category of machine learning, particularly in terms of what types of novel research methodology they both demand and […]
- by Louise LavertyBig Data & Society, Volume 11, Issue 4, October-December 2024. The COVID-19 pandemic response in the UK, as in other countries, drew heavily on health and social care data, making its utility extremely visible as necessary for timely government decision-making and planning. The urgency created by the crisis, however, meant that additional data collection and sharing under emergency legislation was implemented with minimal public consultation. To understand the public perception of these new data measures and initiatives, three citizens’ juries took place in the spring of 2021. This article reports on qualitative observations of the small group deliberations from these […]
- by Sonja TrifuljeskoBig Data & Society, Volume 11, Issue 4, October-December 2024. This article examines heterogeneous forms of human relationalities with algorithms envisioned in the development of a public algorithmic system and their anticipated effects. To do that, we focus on the distinct shapes given to both technologies and people by discourses and practices, together with their underlying logics and associated values. Analysing the blog posts documenting the emergence of Omaolo, a digital platform for healthcare and social welfare in Finland, we identify two algorithmic configurations: the ‘service engine’, which aligns with the public administration goals of standardising social and healthcare services […]
- by Louise MarryatBig Data & Society, Volume 11, Issue 4, October-December 2024. As health data infrastructure improves, we have the opportunity to link increasing volumes of data in order to investigate important health problems. This is perhaps most pertinent when looking at the experiences and outcomes of our most disadvantaged groups, who are often invisible in data obtained through primary research. Whilst these data offer enormous opportunity, there are also ethical implications in their use, which are less frequently discussed than in relation to their qualitative counterparts. As a diverse group of clinicians and academics working across public health, we share our […]
- by Aynne KokasBig Data & Society, Volume 11, Issue 4, October-December 2024. Studies of digital resignation focus on the idea of the corporate cultivation of digital resignation among users, an area of intense importance when examining user data sharing with corporations. To best appreciate the implications of digital resignation in a transnational context, it is important to consider not just resignation by users, but by policymakers. Weak digital policymaking in the US context enables continued digital resignation by users. It also allows for data trafficking, or government directed movement of user data across borders without clear user consent. This paper compares digital […]
- by Zhaoqi ZhouBig Data & Society, Volume 11, Issue 4, October-December 2024. With the popularisation of dockless bicycle sharing in cities around the planet in the recent years, studies have increasingly focused on its intrusion to privacy through the extraction and monetisation of users’ personal data and travel trajectories. This raises the concern of surveillance capitalism that is often embedded within urban mobility platforms. While some research has analysed the business models of dockless bikeshare and identified data extraction as their core value proposition, how bikeshare users themselves perceive and interact with data extraction has so far remained unexplored. Using survey data […]
- by Daniel Leix PalumboBig Data & Society, Volume 11, Issue 4, October-December 2024. The voice biometrics industry is promised today as a new center of digital innovation. Tech companies and state agencies are massively investing in speech recognition and analysis systems, pushed by the belief that the acoustics of voice contain unique individual characteristics to convert into information and value through artificial intelligence. This article responds to this current development by exploring the under-researched datafication of the auditory realm to reveal how the sound of voice is emerging as a site for identity construction by both states and corporations. To do so, we […]
- by Dragoș M ObrejaBig Data & Society, Volume 11, Issue 4, October-December 2024. Political and moral/religious contents are increasingly popular on TikTok, and the concerns associated with them create the premises for a re-exploration of the user–machine agency negotiation. Using algorithmic awareness as a process, this research examines the relationship between users’ awareness of the TikTok algorithm and the main concerns associated with content that conveys political or moral/religious tenets. A survey of 329 Romanian students showed that greater algorithm awareness influences positive attitudes toward algorithms, but significantly stronger positive effects are observed between awareness and the two mediators related to political and […]
- by Terrence Ting-Yen ChenBig Data & Society, Volume 11, Issue 4, October-December 2024. What kind of “democracy” do new government-led digital initiatives facilitate? This paper discusses the issue by investigating the open government data policy in Taiwan in the 2010s, asking whether the policy encouraged “strong democracy.” Using interviews, written records, and an analysis of platform design, I argue that the implementation of Taiwan's open data policy has not institutionalized the engagement of civil society groups or ordinary citizens in government decision-making processes, which is at odds with the claims that open government data encourages “strong democracy.” Instead, open government data in Taiwan […]
- by Itzelle Medina-PereaBig Data & Society, Volume 11, Issue 4, October-December 2024. In this paper, we bring together the concepts of data valences and data journeys to examine how ideational and material factors work together to shape the movement of health data from the UK healthcare sector to universities for reuse in research. Specifically, we focus on the interaction of university-based researchers’ constructs about data with the material conditions of health data circulation in the UK and how these dynamics drive greater circulation of health data through the data sharing infrastructure. Building on our empirical research, we identify four data valences or […]
- by Nada AkrouhBig Data & Society, Volume 11, Issue 4, October-December 2024. In recent years, citizen engagement in policy and research has gained considerable momentum. In the healthcare domain, patient narratives, through various mediums, have emerged as a valuable source of insight into the experiences of patients and the healthcare system. Recognizing the value of such textual data, diverse analytical methods have been developed, spanning from text mining to narrative analysis. This article presents experiments that combine computational methods, qualitative methods and citizen science for analyzing patients’ stories. In this article, we reflect on two experiments in which we combined these approaches, […]