The spreading of COVID-19 misinformation on social media could have severe consequences on people's behavior. In this paper, we investigated the emotional expression of misinformation related to the COVID-19 crisis on Twitter and whether emotional valence differed depending on the type of misinformation. We collected 17,463,220 English tweets with 76 COVID-19-related hashtags for March 2020. Using

Big Data and Artificial Intelligence have a symbiotic relationship. Artificial Intelligence needs to be trained on Big Data to be accurate, and Big Data's value is largely realized through its use by Artificial Intelligence. As a result, Big Data and Artificial Intelligence practices are tightly intertwined in real life settings, as are their impacts on society. Unethical uses of Artificial Intelligence

For better and worse, our world has been transformed by Big Data. To understand digital traces generated by individuals, we need to design multidisciplinary approaches that combine social and data science. Data and social scientists face the challenge of effectively building upon each other’s approaches to overcome the limitations inherent in each side. Here, we offer a “data science perspective” on

Frequent public uproar over forms of data science that rely on information about people demonstrates the challenges of defining and demonstrating trustworthy digital data research practices. This paper reviews problems of trustworthiness in what we term pervasive data research: scholarship that relies on the rich information generated about people through digital interaction. We highlight the entwined

Over the last decade, ‘data scientists’ have burst into society as a novel expert role. They hold increasing responsibility for generating and analysing digitally captured human experiences. The article considers their professionalization not as a functionally necessary development but as the outcome of classification practices and struggles. The rise of data scientists is examined across their discursive

The Online Labour Index (OLI) was launched in 2016 to measure the global utilisation of online freelance work at scale. Five years after its creation, the OLI has become a point of reference for scholars and policy experts investigating the online gig economy. As the market for online freelancing work matures, a high volume of data and new analytical tools allow us to revisit half a decade of online

If you are an anthropologist wanting to use digital methods or programming as part of your research, where do you start? In this commentary, we discuss three ways in which anthropologists can use computational tools to enhance, support, and complement ethnographic methods. By presenting our reflections, we hope to contribute to the stirring conversations about the potential future role(s) of (social)

The ambition and proposal for data modeling of myths presented in this paper is to link contemporary technical affordances to some canonical projects developed in structural anthropology. To articulate the theoretical promise and innovation of this proposal, we present a discrete-event system specification modeling and simulation approach in order to perform a generative analysis and a dynamic visualization

Proponents of precision medicine envision that digital phenotyping can enable more individualized strategies to manage current and future health conditions. We problematize the interpretation of digital phenotypes as straightforward representations of individuals through examples of what we call data inheritance. Rather than being a digital copy of a presumed original, digital phenotypes are shaped

This article sets out the rationale for a United Nations Regulation for Artificial Intelligence, which is needed to set out the modes of engagement of the organisation when using artificial intelligence technologies in the attainment of its mission. It argues that given the increasing use of artificial intelligence by the United Nations, including in some activities considered high risk by the European

Social bots – partially or fully automated accounts on social media platforms – have not only been widely discussed, but have also entered political, media and research agendas. However, bot detection is not an exact science. Quantitative estimates of bot prevalence vary considerably and comparative research is rare. We show that findings on the prevalence and activity of bots on Twitter depend strongly

This paper draws lessons from the COVID-19 pandemic for the relationship between data-driven decision making and global development. The lessons are that (i) users should keep in mind the shifting value of data during a crisis, and the pitfalls its use can create; (ii) predictions carry costs in terms of inertia, overreaction and herding behaviour; (iii) data can be devalued by digital and data deluges;

The social impacts of computer technology are often glorified in public discourse, but there is growing concern about its actual effects on society. In this article, we ask: how does “consent” as an analytical framework make visible the social dynamics and power relations in the capture, extraction, and labor of data science knowledge production? We hypothesize that a form of boundary violation in

The narrative of the digital phenotype as a transformative vector in healthcare is nearly identical to the concept of “data drivenness” in other fields such as law enforcement. We examine the role of a prescription drug monitoring program in California—a computerized law enforcement surveillance program enabled by a landmark Supreme Court case that upheld “broad police powers”—in the interprofessional

This special theme examines the dynamic relationships between production, availability, and usage of Big Data, laying out a research agenda for digital heritage at the time of the ‘data turn’. Over the past 15 years, a proliferation of heritage data has been generated by ‘ecosystems of distributed practices’ enacted by the co-working of bodies, cultural identities, organisational workflows, software

Mechanism design is a form of optimization developed in economic theory. It casts economists as institutional engineers, choosing an outcome and then arranging a set of market rules and conditions to achieve it. The toolkit from mechanism design is widely used in economics, policymaking, and now in building and managing online environments. Mechanism design has become one of the most pervasive yet

One of the key features of the contemporary data economy is the widespread circulation of data and its interoperability. Critical data scholars have analysed data repurposing practices and other factors facilitating the travelling of data. While this approach focused on flows provides great potential, in this article we argue that it tends to overlook questions of attachment and belonging. Drawing

Anantharajah K (2021). Racial formation, coloniality, and climate finance organizations: Implications for emergent data projects in the Pacific. Big Data & Society. doi: https://doi.org/10.1177/20539517211027600

The rapid development of machine-learning algorithms, which underpin contemporary artificial intelligence systems, has created new opportunities for the automation of work processes and management functions. While algorithmic management has been observed primarily within the platform-mediated gig economy, its transformative reach and consequences are also spreading to more standard work settings. Exploring

All datasets emerge from and are enmeshed in power-laden semiotic systems. While emerging data ethics curriculum is supporting data science students in identifying data biases and their consequences, critical attention to the cultural histories and vested interests animating data semantics is needed to elucidate the assumptions and political commitments on which data rest, along with the externalities

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

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 (ML). While previous research has focused on how these systems produce

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

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.

Instagram is currently the social media platform most associated with online images (and their analysis), 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

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

Erratum to “Assessing biases, relaxing moralism: On ground-truthing practices in machine learning design and application”

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

This special theme issue of Big Data & Society presents leading-edge, interdisciplinary research that focuses on examining how health-related (mis-)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 (overexposure to both accurate and misleading information

Medical and public health professionals recommend wearing face masks to combat the spread of the coronavirus disease of 2019 (COVID-19). 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

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

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

Machine learning (ML) 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

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

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

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

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 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

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

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

Artificial Intelligence-as-a-Service (AIaaS) 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

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

Misinformation about the novel coronavirus (COVID-19) is a pressing societal challenge. Across two studies, one preregistered (n1 = 1771 and n2 = 1777), 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 (English, French and German). We find that Go Viral

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

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

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

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

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

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

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

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

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

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

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

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 (Cemaden). We describe four points of focus involved in the creation of a data diary – spaces

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

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

This article examines the social acceptability and governance of emotional artificial intelligence (emotional AI) 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

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

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 (in part) by algorithmically inferring users’ interests from their data and providing advertisers with