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This paper examines the work practices involved in making data legible to machines and machine output legible to humans. The study is based on ethnographic research of a team of art experts at DNArt – a data classification system that features a growing database of art images, a classification scheme, a similarity matching algorithm, and a website that together serve as a consumer judgment device in an emerging online market for art. I analyze interactions from meeting observations, interviews, documentation, and online interaction data to show how non-technical art experts explain and repair sociotechnical breakdowns – when their expectations for similarity between art images and artists differ from the similarity relations produced by the algorithm. By repairing breakdowns, the art experts construct the algorithm anew, as a legitimate revealer of similarity in art. In doing so, the team's repair work is folded back into the black box of the algorithm, rendering it invisible and unacknowledged, sometimes even by the experts themselves. [ABSTRACT FROM AUTHOR]

This article examines the datafication of ageing by drawing on a practice approach toward care. We describe the datafication of ageing as a matter of care, achieved through the local tinkering of actors – technology designers, care staff, older adults, and highlighting the practices necessary to develop, maintain and implement data infrastructures. This paper draws on research conducted in a qualitative interview study in a LTC facility that uses AI-supported sensors to detect, predict and alarm care staff about falls of older residents. 18 interviews with developers, staff, residents and interest groups were conducted, as well as 24 h of participant observation in the care facility. The results reveal how AI-development for older target groups is characterized by absent data on these populations. Designers turn to practices that decontextualize data from the realities of older adults, relying on domain experts or synthetic data. This decontextualization of data requires recontextualization, with staff and older residents ensuring that the system functions smoothly, adapting their behavior, protecting the system from making false decisions and making existing care arrangements 'fit' the databases used to monitor activities in these arrangements. The ambivalent position of older adults in this data assemblage is further highlighted, as their caring practices are made invisible by different actors through ageist stereotypes, positioning them as being too frail to understand and engage with the system. While their bodily behavior is core for the databases, their perspective on and engagements with the operating system are marginalized, rendering some aspects of ageing hyper-visible, and others invisible. [ABSTRACT FROM AUTHOR]

Secondary data collection practices are often opaque to platform users and researchers but known to shape individuals' life chances in significant and unequal ways. In this paper, we articulate a clear relationship between invisible, unwanted data collection and its adverse, downstream consequences for marginalized groups by examining instances in the criminal justice field where social media data function as criminal evidence. We show how social media, as a now common form of courtroom evidence, may structurally work against public defense attorneys and defendants with low socioeconomic status (SES). Drawing on casework interviews with public defenders in New York City, we illustrate the mechanisms by which low-SES criminal defendants are at a disadvantage through overbroad search warrants, asymmetrical cooperation, and prejudicial evidence. We discuss the lessons and implications of our case study for platform privacy and governance research and for the courts. [ABSTRACT FROM AUTHOR]

This paper examines research about Wikipedia that has been undertaken using big data approaches. The aim is to gauge the coherence as against the disparateness of studies from different disciplines, how these studies relate to each other, and to research about Wikipedia and new social media in general. The paper is partly based on interviews with big data researchers, and discusses a number of themes and implications of Wikipedia research, including about the workings of online collaboration, the way that contributions mirror (or not) aspects of real-world geographies, and how contributions can be used to predict offline social and economic trends. Among the findings is that in some areas of research, studies build on and extend each other's results. However, most of the studies stay within disciplinary silos and could be better integrated with other research on Wikipedia and with research about new media. Wikipedia is among few sources in big data research where the data are openly available, unlike many studies where data are proprietary. Thus, it has lent itself to a burgeoning and promising body of research. The paper concludes that in order to fulfil this promise, this research must pay more attention to theories and research from other disciplines, and also go beyond questions based narrowly on the availability of data and towards a more powerful analytical grasp of the phenomenon being investigated. [ABSTRACT FROM AUTHOR]

There are currently no concerted attempts to understand the role of whistleblowers in the new social and political environment created by digital ICTs. Digital ICTs drive an accelerating visibility where elites and citizens constantly acquire new tools to track, surveil, and scrutinize each other. Moreover, these technologies make possible a new kind of invisibility. Increasingly complex modes of digital data production and usage generate grey areas that seem to escape legal jurisdiction and democratic oversight. With their privileged access inside these grey areas, conscientious employees-turned-whistleblowers are likely to become key sources for the disclosure of serious wrongdoing in the coming years. The argument is empirically illustrated through three cases that represent different types of grey areas in advanced democracies: big data surveillance (Edward Snowden), tax havens (Antoine Deltour and the Panama and Paradise Papers), and digital political profiling (Christopher Wylie). [ABSTRACT FROM AUTHOR]

Despite extensive studies on ethical data use and algorithms, little is known about the ethical perspectives of tech workers – insiders of a profession that heavily relies on data and algorithms. Nor have there been sufficient cross-national comparisons of their perspectives. Relying on interviews with 98 tech workers in China and the US, this paper is guided by two questions: (1) What are tech workers' perceptions of big data ethics, and (2) what are the cross-national similarities and differences in China and the US? The study found that there are cross-national similarities in tech workers' cautious enthusiasm about the applications of big data in their work, as well as in their complex and ambivalent ethical perspectives on the use of big data in government digital surveillance. The main cross-national differences occur in tech workers' perceptions of whether big data may reinforce social inequalities. US-based tech workers are concerned about the reinforcement of race and gender-based inequalities through the use of big data, whereas their colleagues in China are optimistic that the use of big data may reduce income-based inequalities across geographical regions. The study's findings have implications for how to leverage tech workers' influence and promote the ethical use of data and algorithms. [ABSTRACT FROM AUTHOR]

If we are to think critically about Big Data initiatives, we must learn to take them apart. This paper explains how to interrogate Big Data, not as large homogenous resources, but as heterogeneous collections with varied and discordant local ties. My argument focuses on the Big Data of media collections: composite digital repositories of texts, images, and video created in different contexts, but brought together online. The primary example used in this paper is the Digital Public Library of America (DPLA), a collection composed of digitized library, museum and archive records from institutions across the United States. I demonstrate how local readings of DPLA data can uncover schemata, errors, infrastructures, classifications, absences, and rituals that have important origins. Moreover, I explain how identifying these local features can support new forms of scholarship, pedagogy, and advocacy in the face of Big Data. For this last point, I use two additional cases: NewsScape, a real-time archive of broadcast news, and Zillow, a marketplace for real estate listings. The range of examples demonstrates how the stakes change from one Big Data initiative to the next. The paper concludes with a set of speculative guidelines for attending to the local conditions in Big Data: get dirty, take a comparative approach, show context, use data to connect people, and create opportunities for the collection of counter-data. When working with Big Data, I argue that thinking locally is thinking critically. [ABSTRACT FROM PUBLISHER]

This paper seeks to illuminate the significance of datafication for anti-poverty programmes, meaning social protection schemes designed specifically for poor people. The conversion of beneficiary populations into machine-readable data enables two core functions of social protection, those of recognising entitled beneficiaries and assigning entitlements connected to each anti-poverty scheme. Drawing on the incorporation of Aadhaar, India's biometric population database, in the national agenda for social protection, we unpack a techno-rational perspective that crafts datafication as a means to enhance the effectiveness of anti-poverty schemes. Nevertheless, narratives collected in the field show multiple forms of data injustice on recipients, underpinned by Aadhaar's functionality for a shift of the social protection agenda from in-kind subsidies to cash transfers. Based on such narratives the paper introduces a politically embedded view of data, framing datafication as a transformative force that contributes to reforming existing anti-poverty schemes. [ABSTRACT FROM AUTHOR]

This paper discusses the relevance of Georg Lukács' 1923 book History and Class Consciousness in the context of digital capitalism. It does so by analysing how Lukács' concepts of the dialectic of subject and object, ideology, reification, reified consciousness matter today in the context of big data and digital capitalism. The essay shows that History and Class Consciousness' critique of reification, ideology, and reified consciousness remains highly topical in the age of digital capitalism and big data. Lukács' analysis allows us to critically analyse how social media, big data, and various other Internet technologies are used as tools of reification. At the same time, Lukács reminds us that only human praxis can establish alternatives. [ABSTRACT FROM AUTHOR]

This paper considers how personal data protections figure into the design process of wearable technology. The data question is becoming especially important in the face of recent innovations in biotechnology that capitalize on the new fungibility of biology and electronics, in which new biotech wearables capitalize on the ability to analyze and track changes in blood, sweat, and tears. Interviews and participant observation with wearable tech designers, data scientists, fashion tech entrepreneurs, and select experts in cybersecurity and intellectual property law, reveal a range of approaches to data protection in design within the culture where wearables are beginning to merge with biotech. While a few respondents were extremely vigilant about protecting consumer’s privacy, the majority felt that when consumers ‘opt in’ to data sharing, they should be cognizant of the risks. From their perspective, it is not necessarily the producer’s responsibility to protect user's personal data. These attitudes present a problematic logic, which leaves users vulnerable to data exploitation. The paper concludes by arguing that this laissez-faire culture is the environment in which wearable biotech is being developed and will be deployed. This emerging technology raises issues about bodies, data, and ownership in crucial need of analysis and critique to push its move into the mainstream toward more equitable and inclusive ends. [ABSTRACT FROM AUTHOR]

This paper explores the relationships of subjects in the context of data and data technologies, and advances an original theoretical framework called Informatic Personhood to better conceptual subjects and their relationships. Because of the enormous structural change that data has contributed to, subjects are sometimes distant and backgrounded in studies of data, despite data having significant impacts on their lives. Data-mediated relationships mean an increased scale to a relationship, with individuals able to connect to much broader contexts of data, but also have these structures reach down to their subjective context through data. Informatic Personhood seeks to capture the dynamics of data present in everyday life, addressing this distance and better conceptualising the scale of data-mediated relationships. This framework has two parts. The first – The Informatic Context – explores salient structural developments around data and conceptualises this as being defined by the presence of 'data interfaces' (that connect individuals to digital contexts), 'data circulation' (trends in the movement and storage of data), and 'data abstraction' (data manipulation practices). The second part concerns the Informatic Person, and the embodied, affective, and sensemaking relationships of individuals occurring across and through the Informatic Context. This framework better addresses the scale of data-mediated relationships, and places subjects firmly in the foreground of how data is understood. [ABSTRACT FROM AUTHOR]

This paper draws on regulatory governance scholarship to argue that the analytic phenomenon currently known as ‘Big Data’ can be understood as a mode of ‘design-based’ regulation. Although Big Data decision-making technologies can take the form of automated decision-making systems, this paper focuses on algorithmic decision-guidance techniques. By highlighting correlations between data items that would not otherwise be observable, these techniques are being used to shape the informational choice context in which individual decision-making occurs, with the aim of channelling attention and decision-making in directions preferred by the ‘choice architect’. By relying upon the use of ‘nudge’ – a particular form of choice architecture that alters people’s behaviour in a predictable way without forbidding any options or significantly changing their economic incentives, these techniques constitute a ‘soft’ form of design-based control. But, unlike the static Nudges popularised by Thaler and Sunstein [(2008).Nudge. London: Penguin Books] such as placing the salad in front of the lasagne to encourage healthy eating, Big Data analytic nudges are extremely powerful and potent due to their networked, continuously updated, dynamic and pervasive nature (hence ‘hypernudge’). I adopt a liberal, rights-based critique of these techniques, contrasting liberal theoretical accounts with selective insights from science and technology studies (STS) and surveillance studies on the other. I argue that concerns about the legitimacy of these techniques are not satisfactorily resolved through reliance on individual notice and consent, touching upon the troubling implications for democracy and human flourishing if Big Data analytic techniques driven by commercial self-interest continue their onward march unchecked by effective and legitimate constraints. [ABSTRACT FROM PUBLISHER]

It is the purpose of this paper to make explicit the methodology (the theory of the methods) by which we conducted research for an Economic and Social Research Council-funded research project on the relationship of values to value. Specifically, we wanted to study the imperative of Facebook to monetize social relationships, what happens when one of our significant forms of communication is driven by the search for profit, by the logic of capital. We therefore wanted to ‘get inside’ and understand what capital's new lines of flight, informationally driven models of economic expansion, do to social relations. Taking up the challenge to develop methods appropriate to the challenges of ‘big data', we applied four different methods to investigate the interface that is Facebook: we designed custom software tools, generated an online survey, developed data visualizations, and conducted interviews with participants to discuss their understandings of our analysis. We used Lefebvre's [(2004).Rhythmnanalysis: Space, time and everyday life. London: Continuum] rhythmanalysis and Kember and Zylinska's [(2012).Life after new media: Mediation as a vital process.Cambridge, MA: MIT Press] ideas about ‘lifeness’ to inform our methodology. This paper reports on a research process that was not entirely straightforward. We were thwarted in a variety of ways, especially by challenge to use software to study software and had to develop our project in unanticipated directions, but we also found much more than we initially imagined possible. As so few academic researchers are able to study Facebook through its own tools (as Tufekci [(2014). Big questions for social media big data: Representativeness, validity and other methodological pitfalls. In ICWSM ‘14: Proceedings of the 8th International AAAI Conference on Weblogs and Social Media (pp. 505–514)] notes how, unsurprisingly, at the 2013 ICWSM only about 5% of papers were about Facebook and nearly all of these were co-authored with Facebook data scientists), we hope that our methodology is useful for other researchers seeking to develop less conventional research on Facebook. [ABSTRACT FROM AUTHOR]

The role of data within international development is rapidly expanding. However, the recency of this phenomenon means analysis has been lagging; particularly, analysis of broader impacts of real-world initiatives. Addressing this gap through a focus on data's increasing presence in urban development, this paper makes two contributions. First – drawing from the emerging literature on 'data justice' – it presents an explicit, systematic and comprehensive new framework that can be used for analysis of datafication. Second, it applies the framework to four mapping initiatives in cities of the global South. These initiatives capture and visualise new data about marginalised communities: residents living in slums and other informal settlements about whom data has traditionally been lacking. Analysing across procedural, rights, instrumental and structural dimensions, it finds these initiatives deliver real incremental gains for their target communities. But it is external actors and wealthier communities that gain more; thus, increasing relative inequality. [ABSTRACT FROM AUTHOR]

Environmental data justice (EDJ) emerges from conversations between data justice and environmental justice while identifying the limits and tensions of these lenses. Through a reflexive process of querying our entanglement in non-innocent relations, this paper develops and engages EDJ by examining how it informs the work of the Environmental Data & Governance Initiative (EDGI), a distributed, consensus-based organization that formed in response to the 2016 US presidential election. Through grassroots archiving of data sets, monitoring federal environmental and energy agency websites, and writing rapid-response reports about how federal agencies are being undermined, EDGI mobilizes EDJ to challenge the 'extractive logic' of current federal environmental policy and data infrastructures. 'Extractive logic' disconnects data from provenance, privileges the matrix of domination, and whitewashes data to generate uncertainty. We use the dynamic EDJ framework to reflect on EDGI's public comment advising against the US Environmental Protection Agency's proposed rule for Transparent Science. Through EDJ, EDGI aspires to create new environmental data infrastructures and practices that are participatory and embody equitable, transparent data care. [ABSTRACT FROM AUTHOR]

The concept of witnessing has been used to explore the construction of evidence and experience in settings of law, religion, atrocity, media, history and science. Recent research has examined how digital technologies may multiply the involvement of remote, non-present and unanticipated actors in the witnessing of events. This paper examines what digital data practices at Amnesty International's Decoders initiative can add to the understanding of witnessing. It introduces the notion of 'data witnessing' with reference to four projects on (i) witnessing historical abuses with structured data from digitised documents; (ii) witnessing the destruction of villages with satellite imagery and machine learning; (iii) witnessing environmental injustice with company reports and photographs; and (iv) witnessing online abuse through the classification of Twitter data. These projects illustrate the configuration of experimental apparatuses for witnessing injustices with data. In contrast to accounts which emphasise the presence of an individual human witness at the scene, Amnesty's data practices are conspicuously collective and distributed, rendering the systemic scale of injustices at a distance, across space and time. Such practices may contribute to research on both (new) media witnessing and data politics, suggesting ways in which care, concern and solidarity may be constructed, structured, extended and delimited by means of digital data. [ABSTRACT FROM AUTHOR]

Problems of bias and fairness are central to data justice, as they speak directly to the threat that 'big data' and algorithmic decision-making may worsen already existing injustices. In the United States, grappling with these problems has found clearest expression through liberal discourses of rights, due process, and antidiscrimination. Work in this area, however, has tended to overlook certain established limits of antidiscrimination discourses for bringing about the change demanded by social justice. In this paper, I engage three of these limits: 1) an overemphasis on discrete 'bad actors', 2) single-axis thinking that centers disadvantage, and 3) an inordinate focus on a limited set of goods. I show that, in mirroring some of antidiscrimination discourse's most problematic tendencies, efforts to achieve fairness and combat algorithmic discrimination fail to address the very hierarchical logic that produces advantaged and disadvantaged subjects in the first place. Finally, I conclude by sketching three paths for future work to better account for the structural conditions against which we come to understand problems of data and unjust discrimination in the first place. [ABSTRACT FROM AUTHOR]

Online social networks are an important setting for understanding the intersection of online communities and offline political processes. This paper analyzes the different ways that people discuss elections on Twitter. Using data from a random sample totaling 113,985 tweets and 30,995 users, we examine the differences between users who employ various strategies to talk about US 2010 Congressional candidates. We show that users who simply include the text of a candidate's name in a message exhibit different behaviors than those who use platform-specific mechanisms. Users who employ free-text tend to have younger accounts, are less likely to be ‘verified’, and generate fewer messages about candidates. Furthermore, candidates’ share of the free-text Twitter public has a larger correlation with their vote tallies than @mentions or hashtags. This research has methodological implications for studying the dynamics between online discourse and offline behavior. Overall, our findings support the view that forms of communication that are more accessible are more indicative of broader social trends. [ABSTRACT FROM AUTHOR]

The author discusses the fundamental points regarding content production and inequality using data from the 2011 Oxford Internet Survey (OxIS). He cites the comment by Jen Schradie about his survey which argued that it represents a break from American digital inequality literature. He adds that combining multiple surveys into a data-set requires assumption of the coefficient which estimates each survey individually.

As the data phenomenon has grown, researchers have increasingly recognized the need to investigate algorithmic and data-driven technology, practices, and culture. While a number of studies have investigated the process of designing algorithms or examined the contours and consequences of algorithms themselves, more research is needed that details the ways in which users of data-driven technology make sense of algorithmic output and the conditions under which these processes unfold. This paper explores how users derive knowledge from predictive algorithms in medical contexts. Drawing upon interviews and observations of a neonatal intensive care unit, I examine how clinicians use data-driven predictive algorithms designed to forecast the onset of infection. Although the developers intend the technology to function as an early warning system, clinicians do not formulate knowledge from the technology solely as intended. Instead, I find that clinicians engage in a set of interpretive processes that I call ‘conditioned reading,’ and ‘accumulative reading.’ I suggest that these processes are possible due to the particular conditions of the medical context, including the institutional entrenchment of evidence-based medicine. I conclude by arguing that fully theorizing the social implications of data analytics will require researchers to investigate the role of institutional contexts. [ABSTRACT FROM PUBLISHER]

The Facebook experiment of 2014 manipulated the contents of nearly 700,000 users’ News Feeds to induce changes in their emotions. This experiment was widely criticized on ethical grounds regarding informed consent. This controversy, however, diverted attention from a more important concern the experiment was intended to address, which is the impact of Facebook use on well-being. In this paper, I explore the well-being concerns raised by prior research and argue that the experiment does not alleviate them, owing to poor research design. As the question of Facebook's impact on well-being is of great importance, both to Facebook and to society overall, there is a pressing need for more experimental research that is both sensitive to informed consent and carefully designed to yield reliable results. In turn, the lessons of this case have implications for general issues of validity that emerge in Big Data research, now in vogue at major scientific venues. [ABSTRACT FROM PUBLISHER]

In the wake of the digital revolution and connected technologies, societies store an ever-increasing amount of data on humans, their preferences, and behavior. These modern technologies create a trust challenge, insofar as individuals have to trust data collectors such as private organizations, government institutions, and researchers that their data is not misused. Privacy regulations should increase trust because they provide laws that increase transparency and allow for punishment in cases in which the trustee violates trust. The introduction of the General Data Protection Regulation (GDPR) in May 2018 – a wide-reaching regulation in EU law on data protection and privacy that covers millions of individuals in Europe – provides a unique setting to study the impact of privacy regulation on trust in data collectors. We collected survey panel data in Germany around the implementation date and ran a survey experiment with a GDPR information treatment. Our observational and experimental evidence does not support the hypothesis that the GDPR has positively affected trust. This finding and our discussion of the underlying reasons are relevant for the wider research field of trust, privacy, and big data. [ABSTRACT FROM AUTHOR]

Digital platforms and learning analytics are becoming increasingly widespread in the education sector: commercial corporations argue their benefits for teaching and learning, thereby endorsing the continuous automated collection and processing of student data for measurement, assessment, management, and identity formation. Largely missing in these discourses, however, are the potential costs of datafication for pupils' and teachers' agency and the meaning of education itself. This article explores the general discursive framing by which these surveillant practices in education have come to seem natural. Through a study of commercial suppliers of educational platforms, we show how the prevailing vision of datafication in their discourses categorises software systems, not teachers, as central to education, reimagining space, time, and agency within educational processes around the organisation of data systems and the demands of commercial data production. Not only does this legitimate the new connective environment of dataveillance (that is, surveillance through data processing), but it also naturalises a wider normative environment in which teachers and students are assigned new roles and responsibilities. In the process, the panoptic possibilities of ubiquitous commercial access to personal educational data are presented as part of a virtuous circle of knowledge production and even training for good citizenship. This broader rethinking of education through surveillance must itself be critiqued. [ABSTRACT FROM AUTHOR]

This article develops a sociotechnical conceptualisation of data literacies in relation to citizens' data practices: highlighting the agentic, contextual, critical, and social aspects of data skills and competencies, it frames data literacies as both discursive and material. In order for civil society organisations to make sense of big, small, open and other data they need multiple skills, beyond the technical; it is, therefore, unhelpful to talk about a single form of data literacy. It is more helpful to consider how such literacies in the plural develop within the material social contexts of civic cultures, and how they can progress in tandem with critical awareness about the power aspects of data, so they can become central tenets of data advocacy. The primary purpose of the article is to move forward the debate around how to conceptualise data literacy – and to question how far the concept is useful in the first place. The article draws on empirical research and starts from the premise that it is imperative to develop frameworks and training schemes that enable civil society actors and publics more generally to use open data, to make data more relevant to stakeholders, and to support their engagement in policy debates around datafication. [ABSTRACT FROM AUTHOR]

The image of big data and algorithms in society is obviously ambivalent. On the one hand, algorithms are seen as a tool of empowerment that allows us, for example, to render society transparent and thus governable, to the extent that the social sciences might even become obsolete. On the other hand, algorithms seem to assume a mysterious agency in the black box of the computer so that their operations are invisible and inscrutable to us: artificial intelligence is seen as something that one day will have the power to dominate us. Beyond these two extreme positions that both overestimate and underestimate how algorithms might change our way of seeing things and being in the world, the present article introduces a third perspective. Algorithms, it holds, indeed follow their own 'style of reasoning' and thus create new realities. At the same time, however, they 'reduce reality', as they lack access to the world of human sense making. Algorithms have no secrets but deploy a 'logic of the surface'. As they paint a behaviorist picture of human modes of existence, algorithms and big data might change our self-understanding. Engaging in epistemological questions will help us to capture the ontological implications of algorithmic reasoning. [ABSTRACT FROM AUTHOR]

This essay explores knowledge claims about Big Data/BD from an ethnographic viewpoint. This epistemological exploration was triggered by social scientist/BD analyst Seth Stephens-Davidowitz' best-selling book Everybody Lies (2017). In my reading, it portrays BD in a way that evokes affinity with ethnography: as a naturalistic research practice that makes visible small subpopulations and discloses people's hidden motives. This threefold assertion rests on misguided conceptions however. To the ethnographic researcher, 'naturalism' refers to a reflexive practice, but the BD researcher associates it with researcher invisibility. The term 'population', which has a statistical meaning in BD, has a theoretical connotation in ethnography. Finally, 'motives' in BD are about direct interpretation of revealed preferences as social facts, whereas the ethnographer considers them to be expressions of social behaviour that require a Verstehende interpretation. A BD revolution may be unfolding, but that does not make ethnography obsolete; ideally, both can be combined in a symphonic social science. [ABSTRACT FROM AUTHOR]

The body of literature on underrepresentation and gender inequality is vast. However, despite its potential to perpetuate gender stereotypes, the overrepresentation of women in media has received inadequate attention. This study explores how traditional news media and social media overrepresent females as drivers when discussing traffic accidents, and whether social media could be the 'new equalizer' for gender. Focusing on China, we collected 97,120 posts from Weibo, China's largest microblogging site, and 11,290 newspaper articles dated between January 2010 and November 2018. We analyzed the data through a mixed-methods design and found that female drivers are overrepresented in discussions of traffic accidents, in both newspapers and on Weibo. While the gender bias against female drivers is more prevalent on Weibo than in newspapers, Weibo has provided a platform for gender-aware discussion. Our study closes by offering suggestions for cross-platform and cross-cultural comparisons of gender representation in the digital age. [ABSTRACT FROM AUTHOR]

The role that technologies have historically played in producing and reproducing global inequalities is well documented. Although technological innovation is associated with progress that does not mean that it necessarily narrows the gap between rich and poor, instead technological inequalities tend to exacerbate other inequalities. This applies also to information and communication technologies (ICT) and Big Data, which play an increasingly important role in humanitarianism. In this article, we address the socio-technical work that is necessary to acquire, process, store and use data and study the power relations that are embedded in these processes. We focus in particular on the use of Big Data in digital humanitarianism and argue that at each stage of the digital data life-cycle (data acquisition, data processing, data storage, and data usage and decision making) different resources are required. These include not only access to hardware, software and connectivity but also the ability to make use of the affordances of digital technologies. We posit that in the context of humanitarianism, ICT and Big Data are a particularly intriguing to study due to their ambivalent position of seeking to address inequalities while at the same time perpetuating them. [ABSTRACT FROM AUTHOR]

This study examined the echo chamber phenomenon and opinion leadership on Twitter based on the 2016 U.S. presidential election. Network analysis and 'big data' analytics were employed to analyze more than 50 million tweets about the two presidential candidates, Donald Trump and Hillary Clinton, during the election cycle. Overall, the results suggested that Twitter communities discussing Trump and Clinton differed significantly in the level of political homogeneity and opinion leadership, and that certain opinion leaders were responsible of creating homogeneous communities on Twitter. This study made a theoretical contribution to the literature by linking opinion leadership and Twitter's network structure and shedding light on what may have caused the echo chamber problem to happen in an emerging media landscape. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editors discuss various reports within the issue on topics including big data, social justice and human rights.

Neither big data, nor data justice are particularly new. Data collection, in the form of land surveys and mapping, was key to successive projects of European imperialist and then capitalist extraction of natural resources. Geo-spatial instruments have been used since the fifteenth century to highlight potential sites of mineral, oil, and gas extraction, and inscribe European economic, cultural and political control across indigenous territories. Although indigenous groups consistently challenged maintained their territorial sovereignty, and resisted corporate and state surveillance practices, they were largely unable to withstand the combined onslaught of surveyors, armed personnel, missionaries and government bureaucrats. This article examines the use of counter-mapping by indigenous nations in Canada, one of the globe's hubs of extractivism, as part of the exercise of indigenous territorial sovereignty. After a brief review of the colonial period, I then compare the use of counter-mapping during two cycles of indigenous mobilization. During the 1970s, counter-mapping projects were part of a larger repertoire of negotiations with the state over land claims, and served to re-inscribe first nation's long-standing history of economic, social and cultural relations in their territories, and contribute to new collective imaginaries and identities. In the current cycle of contests over extractivism and indigenous sovereignty, the use, scope and geographic scale of counter-mapping has shifted; maps are used as part of larger trans-media campaigns of Indigenous sovereignty. During both cycles, counter-mapping as data justice required fusion within larger projects of redistributive, transformative and restorative justice. [ABSTRACT FROM AUTHOR]

Researchers increasingly draw on social media data to answer big questions about social patterns and dynamics. However, as with any data source, social media data present both opportunities and significant challenges. One major critique of social media data is that the data are not generalizable outside of the platforms from which the data originate. Problems of generalizability stem from non-universal participation rates on various platforms, demographically biased samples, as well as limited access to data based on infrastructural constraints and/or user privacy practices. We suggest that instead of empirical generalizability, social media data are theoretically generalizable in the formal theory tradition. Through a case example in which we use YouTube comments to test and extend a key tenet of identity theory, we show how social media data can instantiate theoretical variables and thus generalize to theoretical propositions. Mediated through formal theory, social media data maintain the capacity to address broad social questions while upholding methodological integrity. [ABSTRACT FROM AUTHOR]

It could be argued that the power of data is located in what they are used to reveal. Yet we have little understanding of the role played by the emerging industry of data analytics in the interpretation and use of big data. These data analytics companies act as intermediaries in the digital data revolution. Understanding the social influence of big data requires us to understand the role played by data analytics within organisations of different types. This particular article focuses very specifically upon the way in which data and data analytics are envisioned within the marketing rhetoric of the data analytics industry. It is argued that to understand the spread of data analytics and the adoption of certain analytic strategies, we first need to look at the projection of promises upon that data. The way that data and analytics are imagined shapes their incorporation and appropriation into practices and organisational structures – what I call here thedata frontiers. This article draws upon a sample of 34 data analytics companies in order to explore the way in which data analytics are envisioned within that increasingly powerful industry. [ABSTRACT FROM AUTHOR]

In this study, we explore the role of specific network structures that enhance social capital and assess the extent to which gender, social ties, and communication interaction relate to content popularity within online social networks (OSNs). Our results are based on an extensive OSN data set, containing over 100,000 members, connected by over 1.7 million links. The findings indicate that content popularity inference is more accurate when considering activity interaction among users and that network structures known as advantageous for amassing social capital in the offline environment are relevant online as well. We conclude by discussing how gender mediates the correlation between some network measures and the growth of users’ content popularity and provide a potential explanation for the emergence of gender differences. [ABSTRACT FROM AUTHOR]

This article explores the questions associated with what might be thought of asthe social power of algorithms. The article, which introduces a special issue on the same topic, begins by reflecting on how we might approach algorithms from a social scientific perspective. The article is then split into two sections. The first deals with the issues that might be associated with an analysis of the power of the algorithms themselves. This section outlines a series of issues associated with the functionality of the algorithms and how these functions are powerfully deployed within social world. The second section then focuses upon thenotionof the algorithm. In this section, the article argues that we need to look beyond the algorithms themselves, as a technical and material presence, to explore how the notion or concept of the algorithm is also an important feature of their potential power. In this section, it is suggested that we look at the way that notions of the algorithm are evoked as a part of broader rationalities and ways of seeing the world. Exploring the notion of the algorithm may enable us to see how algorithms also play a part in social ordering processes, both in terms of how the algorithm is used to promote certain visions of calculative objectivity and also in relation to the wider governmentalities that this concept might be used to open up. [ABSTRACT FROM PUBLISHER]

As the era of ‘big data’ unfolds, researchers are increasingly engaging with large, complex data sets compiled from heterogeneous sources and distributed across networked technologies. The nature of these data sets makes it difficult to grasp and manipulate their materiality. We argue that moments of breakdown – points at which progress is stopped due to a material limitation – provide opportunities for researchers to develop new imaginations and configurations of their data sets' materiality, and serve as underappreciated resources for knowledge production. In our ethnographic study of data-intensive research in an academic setting, we emphasize the layers of repair work required to address breakdown, and highlight incremental innovations that stem from this work. We suggest that a focus on the breakdown–repair process can facilitate nuanced understandings of the relationships and labour involved in constituting data assemblages and constructing knowledge from them. [ABSTRACT FROM AUTHOR]

In this essay, I further explicate the construct of affective publics by drawing elements from two case studies, the first focusing on uses of Twitter leading up to and following the events surrounding the resignation of Hosni Mubarak via #egypt, and the second one focusing on online iterations of the Occupy movement, and specifically #ows, one of the more connective and central tags of the movement. I explore what mediated feelings of connectedness do for politics and networked publics in the digital age, and explore their impact on structures of storytelling, sentiment, and the mediality of events broadcast through different platforms. Technologies network us, but it is our stories that connect us. [ABSTRACT FROM PUBLISHER]

The aim of this article is to argue that privacy, rather than serving only as a countermeasure against surveillance, can also be seen as its ‘partner-in-crime’. Normative statements made by governments and companies on privacy can be regarded as a tool of governance in service of informational capitalism. Initially defined as a fundamental freedom, privacy has become a precondition for a blossoming economy in the context of the information society. The notion of privacy, as a critique of information society, has been assimilated and reshaped by and in favour of informational capitalism, notably by being over-individualized through the self-determination principle. To develop this idea, this article builds on the results of a study on the loyalty programmes run by the four biggest retailers of Switzerland and on the Foucauldian concept of biopower. Indeed, sexual liberation and the development of scientific knowledge on sexuality, the democratization of privacy, and the emergence of scientific discourses about privacy are processes that show intriguing similarities. Like sexuality, privacy has become a ‘power-knowledge’ related to moral standards defining what privacy should be. It produces ‘subjects of privacy’ who are supposed to take care of it according to the official conception of privacy advocates and of the legislature. Finally, we suggest understanding the conception of privacy as a terrain of power struggle between the promoters of an informational capitalism based on surveillance of citizens and consumers, and those who would prefer to promote privacy as a common good leading society to more democracy and freedom. [ABSTRACT FROM PUBLISHER]

The era of Big Data has begun. Computer scientists, physicists, economists, mathematicians, political scientists, bio-informaticists, sociologists, and other scholars are clamoring for access to the massive quantities of information produced by and about people, things, and their interactions. Diverse groups argue about the potential benefits and costs of analyzing genetic sequences, social media interactions, health records, phone logs, government records, and other digital traces left by people. Significant questions emerge. Will large-scale search data help us create better tools, services, and public goods? Or will it usher in a new wave of privacy incursions and invasive marketing? Will data analytics help us understand online communities and political movements? Or will it be used to track protesters and suppress speech? Will it transform how we study human communication and culture, or narrow the palette of research options and alter what ‘research’ means? Given the rise of Big Data as a socio-technical phenomenon, we argue that it is necessary to critically interrogate its assumptions and biases. In this article, we offer six provocations to spark conversations about the issues of Big Data: a cultural, technological, and scholarly phenomenon that rests on the interplay of technology, analysis, and mythology that provokes extensive utopian and dystopian rhetoric. [ABSTRACT FROM AUTHOR]