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Datafication, Dataism and Dataveillance: Big Data Between Scientific Paradigm and Ideology
Abstract
Metadata and data have become a regular currency for citizens to pay for their communication services and security--a trade-off that has nestled into the comfort zone of most people. This article deconstructs the ideological grounds of datafication. Datafication is rooted in problematic ontological and epistemological claims. As part of a larger social media logic, it shows characteristics of a widespread secular belief. Dataism, as this conviction is called, is so successful because masses of people-- naively or unwittingly--trust their personal information to corporate platforms. The notion of trust becomes more problematic because people's faith is extended to other public institutions (e.g. academic research and law enforcement) that handle their (meta)data. The interlocking of government, business, and academia in the adaptation of this ideology makes us want to look more critically at the entire ecosystem of connective media.
... Çünkü kullanıcı verileri bir kez kullanılmaya başlandığında algoritmalar yardımıyla bu bilgiler hiç durmadan kullanılabilecektir. En bariz örneği ise Facebook-Cambridge Analytica skandalı olarak karşımıza çıkmaktadır (Dijck, 2014). ...
Dijitalleşme, yapay zekâ ve sosyal medya gibi dar bir çerçeveye sıkıştırılamayacak kadar kapsamlı bir dönüşümdür. Yaratıcı yıkım süreciyle eski sistemleri dönüştürürken, bilgiye erişim, uzaktan çalışma, eğitimde eşitlik ve sağlık hizmetlerinde dijital çözümler gibi fırsatlar sunar. Ancak bu süreç, dijital uçurum, veri güvenliği riskleri, işsizlik endişeleri ve sosyal ilişkilerin zayıflaması gibi sorunları da beraberinde getirir.
Dijitalleşmenin ekonomiye etkisi üretkenlik vagonu teorisiyle açıklanabilir: Teknolojik yenilikler, bazı sektörlerde işgücü talebini artırabilir, ancak bu etkinin adil dağılımı için politik düzenlemeler (karbon vergisi, sendikalaşma, servet vergileri) şarttır. Ayrıca, teknoloji devlerinin aşırı güçlenmesi, demokrasi ve rekabeti tehdit edebilir.
Sonuç olarak, dijitalleşmenin fırsatlarından yararlanırken olumsuz etkilerini minimize etmek için çok yönlü politikalar, şeffaf yönetişim ve toplumsal dayanışma gereklidir. Dijital dönüşüm, ancak insan odaklı ve sürdürülebilir bir yaklaşımla anlamlı hale gelebilir.
... Consequently, we can and should understand modern 'digital' culture as part of a longer era of discrete, modular, and categorical ways of understanding the world and our societies. In many ways what we now call the digital age exists inside of an era of 'datafication' that extends back to 17th-century vital statistics and manual counting machines like the earliest pedometers and odometers (Wernimont, 2019; see also Dijck, 2014). ...
... Sometimes referred to as 'Big Data Policing' this movement towards complex and mutable public-private socio-technical assemblages is also captured in recent discussions of 'platform policing' and 'platformization' see Platforms by Egbert). Undergirded by elements of path dependency and ideological 'dataism' (Van Dijck, 2014) holding that predictive accuracy increases through the compilation, accumulation, and processing of ever larger and more varied datasets, processes of police digitalization exhibit an expansionary logic. ...
... This relation no longer has anything to do with free and active participation in the public sphere, but concerns the enrolment and the filing away of the most private and incommunicable aspect of subjectivity: I mean the body's biological life. (Agamben, 2004) We find ourselves in an era characterized by an overwhelming influx of information, often referred to as datafication, a 'transformation of social action into online quantified data' (Van Dijck, 2014), or a 'quantification of the social' (Couldry & Mejias, 2019). This perspective is based on the conviction that data remains impartial and objective and that the systematic use of data can gain insights into various aspects of our social lives and behaviours (Andrejevic, 2013). ...
In today's digital era, algorithmic surveillance has become a significant concern, with automated technologies increasingly deployed by social media platforms and government agencies to monitor individuals and groups. The rise of artificial intelligence prompts critical inquiries into its implications, necessitating an examination of the surveillant nature of algorithms. This scrutiny can unveil power dynamics that foster domination or control [Murphy, 2017. Algorithmic surveillance: The collection conundrum. International Review of Law, Computers & Technology, 31(2), 225-242] and assess their societal impacts on human rights, privacy, and democracy. We employ the framework of Science and Technology Studies (STS) to answer how do imaginaries of neutrality and objectivity construct biopolitics of control and power through disciplinatory technologies over the digital citizen in Latin America. We focus on unravelling the political economy of biometric citizenship and the use of the body for identification and categorization, sorting individuals by utility and legitimacy [Ajana, 2012. Biometric citizenship. Citizenship Studies, 16(7), 851-870]. Through this lens, we aim to challenge deterministic narratives regarding technological change and public policy.
... However, for many new technologies, this assumption no longer appears valid; the proliferation of cloud connectivity and use-data collection practices has fundamentally altered how users engage with technologies. Through a proliferation of multi-directional data flows, designers can increasingly observe the ways that their products are being adopted and utilized in real time (van Dijck 2014). Product updates allow designers to develop and implement changes that respond to use-patterns that they observe, giving them control over a technology's stabilized interpretation and use. ...
Over the past decade, new technologies have been increasingly deployed in a manner that allows designers to remotely collect usage data, facilitating the development and rollout of updates and adjustments after their release. By retaining control over product stabilization, designers can discourage technologies from being used in manners other than that which they prescribe, thereby reducing interpretive flexibility. As a result, end users are increasingly shepherded towards use-patterns that reflect the interests of designers. This paper explores this agential shift using three case studies. The first considers the evolution of a video game series, exploring how expanding data-collection practices in subsequent releases changed design processes and user experiences. The second case examines the evolution of social media design and the rise of algorithmic nudging. The third case broadly analyzes humanitarian design, demonstrating how dataveillance has expanded beyond consumer electronics. By maintaining control over use-patterns, designers can reduce uncertainty and increase profitability. However, these subtle power shifts also have consequences for user agency and interpretive flexibility, reanimating debates about technological determinism.
... In the era of digital economy, digital platforms, as a new type of organization with data as the main factor of production, are bursting with strong development momentum. Through the accumulation of online and offline industrial elements, it breaks the boundaries between virtual and real, subverts the traditional consumption and production modes [4] in the industrial era, and effectively integrates industrial resources and market resources [5]. Currently, most digital platform enterprises use algorithmic decision-making based on big data, which not only improves the operational efficiency of enterprises, but also enhances the experience and satisfaction of platform users. ...
With the iteration of information technology and the enhancement of the arithmetic power of huge amounts of data, the economic model of digital platforms driven by data has become an important carrier of digital trade and digital economic development. By the reason of the dynamism, cross-border and complexity characteristics compared with traditional physical platforms, the development of digital platforms is no longer limited to the level of specific commodity goods or services, and the traditional regulatory system cannot match and adapt to the new attributes of digital platforms them. Therefore, the construction of a flexible, diversified and pluralistic co-management hybrid regulatory model is imminent. This article constructs small-world model to study government regulation of digital platforms. The small world model can effectively describe the connectivity between nodes (users, platforms, regulators, etc.) in the ecosystem of digital platforms, which helps researchers to understand the complexity of the network structure and the propagation pattern of information in the network and design more effective regulatory strategies. In addition, small-world models can be used to assess the robustness of regulatory measures in different scenarios, and to study the stability and information flow capability of the network in the face of node failure or attacks, reflecting the interaction between users and platforms. The research results and relevant discussion are as follows: First, the simulation results of government regulatory intensity indicate the government’s regulatory strength for the algorithmic black-box problem needs to be kept at an appropriate standard. Second, the simulation results government punishment intensity show that we can not only rely on a single regulatory means, but also need to take the composite strategy as far as possible, encouraging multiple bodies to participate in the regulation and collaborative governance to construct a fair and efficient digital transaction environment. Finally, the dynamic simulation results of sample size variation suggest that the government needs to adopt a variety of means to cooperate in forming an efficient and reasonable regulatory system to regulate digital platforms in accordance with the law, avoiding one-size-fits-all regulatory strategies.
... The idea of Big Data goes beyond the collection of special properties that make data "big" or the tools that facilitate its use. The term "Big Data" refers to the varied and conflicting sets of beliefs about the societal impacts of collecting, analyzing, and applying large, real-time, and often interlinked data to influence social processes (boyd & Crawford, 2012;van Dijck, 2014). Stakeholders hold a wide variety of beliefs about whether Big Data can provide reliable strategies for producing accurate and actionable insights for promoting human flourishing. ...
Big Data can provide useful insights for advancing social work research and practice, although the collection of large amounts of personal data, linkage of disparate data sources, and advances in artificial intelligence (AI) pose significant privacy risks to individuals in broader society, and particularly those served through health and social care settings. The conversion of more aspects of everyday life into data, coupled with the increased capability to store and link data, creates new risks for private data breaches, data leakage, re-identification, and other privacy concerns. Despite the potential benefits of Big Data, many social workers still serve within agencies that lack the technical resources, relative to workers in other industries, such as marketing firms or political campaigns, to leverage data efficiently to plan, design, implement, and evaluate programs. As Big Data become more ubiquitous, ethical guidelines have evolved to address issues related to privacy in information and communication technologies. Yet with rapid changes in technology, particularly with respect to AI, social workers may find it difficult to stay abreast of current measures for best safeguarding client privacy and preventing Big Data-related harms.
... Live or retrospective forms of facial recognition can also be understood as a kind of dataveillance (Van Dijck, 2014). Many social media platforms use FRTs and biometrics to determine our identities. ...
... Frameworks also highlight the importance of datafication and dataveillance referring to the presence of big data and surveillance methods throughout cybersecurity mentioning live threat monitoring and identification. Risk control is an important part of data governance that makes use of data analytics together with ongoing monitoring in the identification of possible risk openings that further extend to becoming risk threats waiting to be leveraged by the attackers [5]. ...
The cybersecurity threat landscape is constantly actively making it imperative to develop sound frameworks to protect the IT structures. Based on this introduction, this paper aims to discuss the application of cybersecurity frameworks into the IT security with focus placed on the role of such frameworks in addressing the changing nature of cybersecurity threats. It explores widely used models, including the NIST Cybersecurity Framework, Zero Trust Architecture, and the ISO/IEC 27001, and how they apply to industries including finance, healthcare and government. The discussion also singles out such technologies as Artificial Intelligence (AI) and Machine Learning (ML) as the core for real-time threat detection and response mechanisms. As these integration challenges demonstrate, the study provides tangible and proven approaches to tackle framework implementation issues such as legitimate security issues, limited availability of funds and resources, and compliance with legal requirements. By capturing current trends and exposures, the findings promote strong, portfolio-based and risk-appropriate security approaches adjusted for organizational goals and capable to prevent advanced cyber threats.
... Teknolojik çözümcülük olarak ifade edilen bir bakış açısına sahip olan teknoşovenizm, teknolojinin derin sosyo-ekonomik nedenleri olan sorunları dahi çözebileceği varsayımını kabul etmekte, hatta kimi zaman aslında sorun olmayan sözde sorunlara dahi cevap olarak önerilerek "çekiçli biri için her şey çivi gibi görünür" özdeyişinin uygulamadaki yansımasını oluşturmaktadır (Morozov, 2013). İnsanların yaşamlarını, davranışlarını ve etkileşimlerini izleme ve tahmin etmeye dayalı analize imkân veren niceliksel verilere dönüştürme sürecini temel alan teknoşovenizm, söz konusu verileştirme sürecinin nesnelliğine yönelik büyük bir bağlılık barındırmaktadır (Van Dijck, 2014). Zira teknoşovenizmde veriler, değişmez gerçekler olarak düşünülmektedir. ...
Teknolojik uygulamaların hayatın her alanına kolektif bir istekle entegre edildiği sistemi anlatan “teknoşovenizm”, sorun yeterince iyi tanımlanmışsa, teknolojinin tüm sorunları etkili ve en iyi şekilde çözebileceğini savunmaktadır. Teknoşovenizmin yapı taşlarından birisini ise yapay zekâ teknolojisi oluşturmaktadır. Ulusal ve uluslararası ölçekte, sosyal ve siyasal hayatta büyük değişimler yaratan yapay zekâ, bilginin en büyük sermaye ve servet kaynağı olduğu bir çağda, tüm ulusların rekabet gücünü belirleyen stratejik bir unsurdur. Bu nedenle yapay zekânın doğru kullanımı, aynı zamanda demokrasilerin geleceğini etkileyebilme ihtimali açısından önemlidir. Zira yapay zekâ, özellikle siber ortamda varlık gösteren vatandaşların karar alma süreçlerini şekillendirebilmekte, kolektif karar almanın nasıl gerçekleşeceğini yönlendirebilmektedir. Yapay zekâ teknolojilerinin demokratik değerleri, süreçleri ve vatandaşların sosyal ve siyasi davranışlarını etkilediğine yönelik giderek artan söz konusu kanaat, bu teknolojinin demokratik hayatın sürdürülebilirliği açısından risk ve fırsatlarıyla tartışmaya açılmasını bir gereklilik olarak ortaya koymuştur. Çalışmada yapay zekânın siyaseti yeniden şekillendirmeye yarayan yeni ve farklı yollar yarattığı tezinden hareketle demokrasinin bu uygulamalardan ne ölçüde etkilendiği tartışılmış, yöntem olarak nitel araştırma tekniklerinden metin analizi kullanılmıştır. Çalışmanın amacı demokrasi ve yapay zekânın doğal müttefikler mi olduğu, yoksa yapay zekânın demokratik amaçlara hizmet edebilmesi için aktif tasarım tercihlerine mi ihtiyaç duyulduğuna yönelik değerlendirme yapmaktır.
... The concept of data colonialism was originally introduced by Thatcher, who use the metaphor to bring attention to the power asymmetries in the capitalist 'processes of accumulation by dispossession and colonisation of the lifeworld through the commodification and extraction of personal information as data' (Thatcher et al., 2016, 990). Other authors have referred to corresponding patterns of appropriation and exploitation of data and human labour as 'datafication' (Ruckenstein, 2023;Van Dijck, 2014) or 'extractivism' (Crawford, 2021;Mezzadra & Neilson, 2017), or more broadly, e.g. 'surveillance capitalism' (Zuboff, 2019) or 'platform capitalism' (Srnicek, 2016). ...
... Examples of novel fields of application in the course of the implementation of the process of datafication are creditworthiness checks, portfolio analysis, marketing campaigns, or consumer behavior, among many others, and many more options in the future [24]. The dematerialization, liquification, and compression of ever larger, more numerous, and more heterogeneous data sources are leading to a gradual normalization of the process of datafication in society, as it increasingly enters people's everyday lives in the form of the Internet of Things (IoT), for example, and is taken for granted [25]. The unpredictability of the medium-and long-term interaction of big data and datafication underlines the relevance of big data. ...
... The proliferation of datafication, characterized by the escalating digitization of various aspects of daily life, has amplified the significance of data in governmental administration and commercial operations (van Dijck, 2014). This study endeavours to elucidate the dynamics of data capitalism and its ramifications for users of online communication platforms. ...
As a result of the emergence of the internet, semantic web and social media, netizens have been able to create digital footprints on scholarly, commercial or non-scholarly platforms. However, it remains a mystery as to what happens to the data created by ordinary users of digital platforms, whether they create text, visuals, sounds or multimedia files. Despite the privatization and commoditization of digital spaces by informational
capitalism, austerity policies across the globe have led to deterioration and diminishing of physical public spaces
and infrastructure for providing services. This article seeks to explore the concept of data capitalism amidst rising surveillance capitalism and suggests ways through which libraries can protect the interests of their users against data capitalism
Was ist die Zukunft der Medien? Wie werden die Medien in Zukunft aussehen? Diese beiden Fragen drücken ein erstes Verständnis des Verhältnisses von Medien und Zu-kunft aus: Es geht um die weitere Entwicklung der Medien wie auch die Medien, die noch kommen werden – als Einzelmedien, als Medienrepertoires und Medienensem-bles sowie als Gesamt der Medienumgebung. Ein solches erstes Verständnis wird durch den Alltagsgebrauch des Ausdrucks Zukunft nahegelegt, wonach diese das auf jemanden Zukommende meint, also die Zeit, die noch bevorsteht. Während ein sol-cher Alltagsbegriff ein erster Ausgangspunkt für die Beschäftigung mit dem Themen-feld Medien und Zukunft ist, reicht er doch nicht aus, dieses hinreichend zu fassen. Dies hängt nicht zuletzt damit zusammen, dass aus sozialwissenschaftlicher Sicht das, was wir Zukunft nennen, wesentlich vielschichtiger ist, als ein einfaches Verständnis von „die Zeit, die noch bevorsteht“ suggerieren mag.
This article considers how actors within digital markets creatively navigate moral ambiguities in their work, and how this shapes the building of (software) material relations. Catching the attention of an app user is considered morally ambiguous behavior in the ‘attention economy’ discourse shaping the Danish app market. Employees of the Danish app agency Monocle navigate this moral ambiguity by ‘personalizing’ apps so that they become relevant at the ‘right time’. However, in app-building practices, this entails a set of quite different personalized configurations. Consequently, I argue that personalization works to mediate different values within digital markets. It thereby allows app makers to pursue projects of fashioning themselves as makers of apps that are ‘good’, both in a technical and moral sense, while making apps that also ‘win the market’.
The open big data paradigm has reshaped citizen engagement, promoting digital participation in scientific and policy processes. Transport planning – a field dominated by technical expertise – reveals the specialised dispositions and skills needed to navigate the politics of open big data. Using a Bourdieusian lens and a case study in Hong Kong, this paper explores citizen empowerment within the open big data landscape, focusing on the symbolic aspects of data use. As citizens gain data access, they increasingly adopt professional practices valued in transport planning, aligning with the field's evidence-based standards. Despite expanded citizen participation, findings indicate that much of this engagement remains symbolic. Industry insiders, acting as cultural intermediaries and ‘historic firsts’, symbolically empower citizens by representing them, challenging stereotypes that portray citizens as uninformed. This representation shapes a collective identity, framing citizens as legitimate political agents. Access to authorised open big data sources as symbolic capital, with field-specific linguistic practices, further enhances these intermediaries’ legitimacy. Conceptualising data competencies as part of habitus – internalised schemas of behaviour – reveals that competencies extend beyond analytic skills to include navigating, interpreting and presenting data within accepted field norms, often unconsciously. Exposure to confidential information and data, though not directly usable, becomes a crucial factor in shaping habitus, enabling intermediaries to interpret open data in ways that resonate with authorities. Ultimately, while activists seek to secure their positions in transport planning, they risk reinforcing existing power structures, limiting broader citizen inclusion. This study underscores the need to address these dynamics to foster an inclusive approach to citizen participation in data-centric fields.
This paper examines how the social ontology that underpins the international human rights framework is being challenged by the affordances of AI/ML systems. To set the stage, this paper adopts a socially situated understanding of human rights-acknowledging the socially embedded nature of individuals within societies. Drawing upon Gould's theory on the social ontology of human rights, the individual is not only socially embedded but it is this social situatedness that enables the exercise of positive agency (including moral and political agency). The role of human rights is then to preserve conditions that enable the exercise of such capacities. While the ubiquity of computational technologies such as AI systems may prima facie seem to embrace and operationalise sociality, the paper highlights three pressure points, arguing that they lead towards the structural atomisation of individuals in ways that are in tension with the normative aims of international human rights law. Data points that group, infer and construct individuals through their likeness, atomise individuals as means to an end through AI/ML systems. Further, the efficiency-driven framing of AI/ML reliant on computational tractability means that individuals risk instrumentalisation through optimisation. Finally, the AI/ML mediated shaping of epistemic and enabling conditions can lead to contextual atomisation-threatening the condition antecedent for socially situated exercise of moral agency and with it, human rights. In diagnosing these structural challenges, this paper provides a deeper mapping of the problem space to inform AI/ML and human rights scholars and practitioners of better accounting for the social ontology of human rights in our computational environments.
Since the early 2010s, humanitarian donors have increasingly contracted private firms to monitor and evaluate humanitarian activities, accompanied by a promise of improving accountability through their data and data analytics. This article contributes to scholarship on data practices in the humanitarian sector by interrogating the implications of this new set of actors on humanitarian accountability relations. Drawing on insights from 60 interviews with humanitarian donors, implementing agencies, third-party monitors and data enumerators in Somalia, this article interrogates data narratives and data practices around third-party monitoring. We find that, while humanitarian donors are highly aware of challenges to accountability within the sector, there is a less critical view of data challenges and limitations by these external firms. This fuels donor optimism about third-party monitoring data, while obscuring the ways that third-party monitoring data practices are complicating accountability relations in practice. Resultant data practices, which are aimed at separating data from the people involved, reproduce power asymmetries around the well-being and expertise of the Global North versus Global South. This challenges accountability to donors and to crisis-affected communities, by providing a partial view of reality that is, at the same time, assumed to be reflective of crisis-affected communities’ experiences. This article contributes to critical data studies by showing how monitoring data practices intended to improve accountability relations are imbued with, and reproduce, power asymmetries that silence local actors.
This panel delves into the complex entanglement of children within our 'platform society,' spotlighting their roles amidst the dynamics of platformisation, datafication, and monetisation. It scrutinises the emergence of child influencers, mapping their integration and active participation within the influencer economy. This exploration underscores the critical intersection between childhood and platformization, highlighting the commodification and monetization practices shaping children's presence on platforms. The panel seeks to understand these practices across diverse contexts, including time, platforms, geographies, and cultures, emphasizing the multifaceted roles of children as consumers, producers, and actors. The panel also examines regulatory frameworks surrounding children on platforms, focusing on governance issues related to child labour, advertising, and platform liability. It navigates the tension between viewing children as 'becomings' in need of protection and as 'beings' with an agency, contributing to the discourse on platform governance and regulatory practices. Presenting a range of papers, the panel traverses topics from the monetization of children in influencer content on TikTok, to the offering of a child influencer taxonomy, to the influence of kidfluencers on young viewers' consumption behaviors. It critically assesses the impact of digital dashboards on parenting, illustrating how children's health, location, and well-being are intertwined with datafication logic. Collectively, these papers illuminate how the place of children online is contested, as they become embroiled in practices of monetisation, visibility, and datafication across platforms and infrastructures. Our panellists draw from their empirical and theoretical work to challenge these practices, emphasising their implications for platform governance and addressing how regulations can serve children's best interests.
Digital transformation fundamentally reshapes industries, public infrastructure, and urban environments by integrating advanced information technologies-such as big data analytics, the Internet of Things (IoT), cloud computing, and machine learning-into everyday operations. This convergence is driving service innovation and productivity gains and introducing new vulnerabilities that require robust cybersecurity measures. This paper reviews and synthesizes findings from a broad literature covering cybersecurity in water and energy infrastructure, big data applications in smart cities and manufacturing, and the policy frameworks evolving to secure critical infrastructures. The overarching narrative demonstrates that digital transformation is a double-edged sword. While it provides enhanced operational capabilities and improved decision-making, it creates new risk vectors in cyber-physical systems. In discussing digital transformation across sectors such as water management, energy, and manufacturing, this paper explores methodologies ranging from probabilistic risk assessment and event-fault tree analysis to cloud-based analytics platforms and systematic literature reviews. This study argues that future research must focus on integrating digital innovation with resilient cybersecurity strategies that protect the infrastructure upon which modern society depends. Finally, the study discusses policy implications and provides directions for future research that can help bridge the gap between technological capabilities and robust risk management.
Esta investigación analiza críticamente el actual contexto sociopolítico y económico de vigilancia masiva para reconstruir las claves y condiciones de posibilidad que orientan su desarrollo en sentido justo y responsable. El estudio, por un lado, advierte del peligro de los impactos disruptivos que produce sobre la sociedad y sus diferentes esferas funcionales la vigilancia social masiva ejercida por los estados y las grandes corporaciones tecnológicas. Por otro, de forma más concreta, sugiere que esta vigilancia social masiva está dando lugar a prácticas despóticas que pervierten los procesos democráticos, reducen los espacios de libertad y aumentan la brecha de las desigualdades. Finalmente, desde sus presupuestos normativos y condiciones de posibilidad, ofrece orientaciones para hacer frente a la vigilancia social masiva: la promoción de una sociedad civil fuerte, dinámica y crítica que actúe como contrapoder frente al estado y las grandes tecnológicas.
This article presents a robust defense of the concept of ‘datafied enhancement’ as a subset of human enhancement. Firstly, the author notes that the widespread development of ICT and AI technologies has made it possible to collect and analyse human biometric data, thus integrating data into the concept of embodiment. Secondly, the author explores the cultural and intellectual history of datafied enhancement, highlighting the significant role that data has played in human evolution. Thirdly, the author examines the functional classifications of datafied enhancement, arguing that conversational, wearable, ingestible and embeddable technological devices can enhance physical, cognitive, emotional and moral capacities of individuals by collecting body data. Fourthly, the author underscores the importance of distinguishing datafied enhancement from dataism to avoid misconceptions. Finally, the author addresses potential ethical issues, such as data exclusivity, the uncontrollable data self, and privacy invasion. By acknowledging these risks, datafied enhancement can be guided to become a technology that respects human dignity and promotes the common good.
Scientific and media discourse on the Internet of Things (IoT) emphasizes privacy concerns as a possible hurdle to widespread adoption. Drawing on a study conducted with users of home IoT devices and stakeholders, this article examines their perceptions, attitudes, and practices through the framework of privacy concerns, privacy rationales, and privacy work. The results show that users are ambivalent regarding data collection and transfer by IoT devices, oscillating between the advantages of personalization and fears of data commercialization, often accompanied by feelings of powerlessness and privacy resignation. Privacy rationales frequently translate into privacy work that includes rejection, digital housekeeping, and finding appropriate locations in the home. As IoT adoption expands beyond higher educated and higher skilled users, more effort should be made in regulating products and protecting citizens from the increased datafication of everyday life.
A efetivação da transparência governamental por meio do acesso de informações públicas perpassa por diversos desafios no Brasil. Nesse sentido, membros da sociedade civil intitulados de cidadãos vigilantes desenvolveram iniciativas de monitoramento que vigiam e denunciam possíveis falhas na condução da Lei de Acesso à Informação no país. O presente artigo analisou como esses projetos vêm se atualizando com o uso de estratégias que adaptam softwares e Inteligência Artificial para burlar entraves que dificultam a continuidade da vigilância civil. A pesquisa exploratória resultou na verificação de projetos consolidados que adaptaram mecanismos tecnológicos para a manutenção de propostas comunicativas no monitoramento.
Esta investigación analiza críticamente el actual contexto sociopolítico y económico de vigilancia masiva para reconstruir las claves y condiciones de posibilidad que orientan su desarrollo en sentido justo y responsable. El estudio, por un lado, advierte del peligro de los impactos disruptivos que produce sobre la sociedad y sus diferentes esferas funcionales la vigilancia social masiva ejercida por los estados y las grandes corporaciones tecnológicas. Por otro, de forma más concreta, sugiere que esta vigilancia social masiva está dando lugar a prácticas despóticas que pervierten los procesos democráticos, reducen los espacios de libertad y aumentan la brecha de las desigualdades. Finalmente, desde sus presupuestos normativos y condiciones de posibilidad, ofrece orientaciones para hacer frente a la vigilancia social masiva: la promoción de una sociedad civil fuerte, dinámica y crítica que actúe como contrapoder frente al estado y las grandes tecnológicas.
Business process mining has become a revolutionary approach towards understanding various aspects of a business processes that can lead to improve efficiency. Event logs and other complex algorithms can be used to create visual representations of the events and models of organization's work. This paper identifies the benefits of process mining in business process management although it puts emphasis on discovering ineffectiveness, compliance check, and decision making in a particular organization. A review of literature coupled with the use of real world case in different domains gives an insight into various advantages and disadvantages as well as the future of this technology.
This study addresses the potential impact of recommendation algorithms on Spotify users' musical tastes with the aim of understanding how algorithmic suggestions shape listening behaviors and preferences. A comprehensive review of the literature reveals that the presence of algorithms has contributed to reduced musical diversity and increased taste tautology among users. The results suggest that recommendation algorithms reinforce prior preferences, leading to the emergence of filter bubbles This algorithm-driven taste has obvious cultural implications and, with it, a large impact on the overall diversity of the musical experience. A qualitative methodology was used, consisting of a systematic literature review based on the PRISMA framework, identifying trends and key elements of existing studies. This study finds its limitations in the need for an additional quantitative study to delve deeper into the behavior of recommendation algorithms. Ultimately, this research underscores the need for greater awareness of the implications of music recommendation using algorithms in the digital age.
When urban landscapes erupt into civil unrests, smart technologies that are intended to help preserve social order may become prime sites of contention. Integrating critical data studies and research on networked social movements, this article examines the underexplored contours of networked disobedience to smart city development – that is, direct action by self‐mobilised and self‐organised digitally connected citizens and activists to subvert or disrupt the dominant structure of the datafied smart city – during a large‐scale protest movement. The case of Hong Kong's smart lampposts is analysed to explicate a distinct technopolitical contention that emerged in the digital age, focusing on three key aspects: (1) citizens' digital curation of folk theories, which perpetuated a consensus of discontent over the installation of smart city technology, (2) the articulation of a digitised network of counter‐power that provided a mediation opportunity structure for mobilisation and intervention, and (3) the crowdsourcing of disobedient practices of data activism aimed at sabotaging or evading the smart city technology. The article illustrates how seemingly ordinary issues of urban datafication can be repurposed to (re)produce political contention and the ways in which controversies over smart city development may fuel adversarial citizen–state engagement with repercussions for data‐driven urban governance.
In 2020, Uber celebrated its presence in over 10.000 cities around the world. Such global scaling and granular infiltration of algorithmic-powered management of labour and urban dynamics are achieved through efforts to datafy and visualise cities, making them intelligible under a unified vision. While workers on the ground precariously move across territories, Uber’s engineering teams invest in abstract and disembodied views of the city. Taking Uber’s knowledge production about cities as an object of inquiry, this paper explores the epistemic dimensions of platformisation, delving into platforms' technical and narrative reliance on datafication. Through the analysis of 41 publications on Uber Engineering Blog (UEB), it delves into Uber's textual and visual data stories on knowing cities, highlighting the contrast between a lively community of practitioners and industry, and the neglect in acknowledging platform workers' labour and needs.
This paper draws upon conceptual frameworks of platformisation (van Dijck, Poell, and de Waal, 2018), media convergence (Jensen, 2022), trust in digital banking (Mezei and Verteș-Olteanu, 2020; van Esterik-Plasmeijer and van Raaij, 2017), and social imaginaries (James, 2019; Mansell, 2012; Gillespie, 2018). It views digital banking apps as platforms that enable personalised interactions (Poell, Nieborg, and van Dijck, 2019), and aim to investigate the datafication (van Dijck, 2014; Sadowski, 2019) and platformisation of banking. This approach underscores the transformation of service dynamics and the challenges brought by digital banking concerning public accessibility and social inclusion (Swartz, 2020). We ask: a) What are the dominant imaginaries of payment reflected by contemporary financial services? and b) How do the design and affordances of digital payment services impact trust, responsibility, and user labour? This paper employs a modified walkthrough method (Light, Burgess, and Duguay, 2018) including detailed content analysis of the Terms and Conditions (T&Cs) documents required for initial access to seven digital banking apps in Ireland. The sampled banking apps include Bank of Ireland (BOI), N26, An Post Money, Revolut IE, Chase UK, Starling Bank UK, and Klarna. The modified walkthroughs highlight a significant convergence between the finance and media industries. Our analysis identified three dominant social imaginaries of payment leading to different designs for digital banking apps: a) the Institutional Imaginary, b) the Transactional Imaginary, and c) the Digital Imaginary.
Netflix is often credited with mainstreaming binge-watching through its release strategy and interface features. However, despite this reputation, data on actual consumption patterns remains scarce, enabling Netflix to shape the narrative about how content is consumed on its platform and what this implies about content quality and viewer attentiveness. This article provides unique empirical insights into Netflix viewing patterns in the Netherlands, based on a pilot study involving data donated by 126 subscribers. It introduces a definition of binge-watching tailored for computational analysis and offers an empirical understanding of its prevalence and manifestations. The findings suggest that binge-watching is a diverse and complex activity. While it is seemingly popular, in that it is practiced by many subscribers, the data suggest it occurs less frequently and is less extreme than would be expected from the hype.
L'articolo analizza le condizioni affinché l'e-portfolio delle competenze, supportato dall'intelligenza artificiale generativa (Generative Artificial Intelligence - GenAI), rappresenti uno strumento pedagogico innovativo nell'istruzione superiore per favorire un migliore allineamento tra l'offerta di lavoro ad alta qualificazione e la domanda di competenze spesso svalutata. Grazie all'IA, è possibile modellizzare l'e-portfolio in modo più personalizzato ed equo, valorizzando le competenze trasversali e le microcredenziali, riducendo il rischio di standardizzazione eccessiva e quindi di disumanizzazione. Questo strumento consente agli studenti di documentare e riflettere sulle proprie capacità, migliorando la loro visibilità nel mercato del lavoro. Tuttavia, emergono problematiche etiche, come l'uso inappropriato degli e-portfolio da parte dei datori di lavoro per fini esclusivi, e la mancanza di trasparenza negli algoritmi che potrebbero discriminare profili atipici. La GenAI, se utilizzata in modo responsabile, può contribuire a rendere l'e-portfolio uno strumento inclusivo, riducendo le disparità e favorendo una valutazione più obiettiva e imparziale delle competenze.
Big Data has been increasingly implemented in police departments. In the Judiciary Police in Portugal, although it is at an early stage of implementation, there are no studies on the topic. Based on 16 interviews with members of the Judiciary Police, their frames that portray Big Data’s benefits and harms in criminal investigations are explored. The benefits portrayed are related to its capabilities to help fight organized and transnational crimes, advance criminal investigations, and expand the availability of sets of information. The harms focus on the lack of regulatory documents, threats to human rights, and the probability of obtaining erroneous conclusions. A critical analysis of these frames may contribute to reflecting on their role to inform technology developments in policing settings, with implications for inequalities, and crime control.
In a post- Roe America, abortion-rights activists are scrambling to protect reproductive rights. Communication on open social media platforms like Facebook can now be used to prosecute those seeking an illegal abortion in a frightening entanglement of technology corporations and the state. Building on surveillance studies and feminist scholarship, we analyze 22 interviews with pro- and anti-abortion activists and pro-encryption activists to answer: How will encrypted messaging apps be relevant for pro- and anti-abortion activists post- Roe? We find that while encrypted messaging apps are used by activists on either side of the abortion debate, their motivations for use range from mere convenience to supplanting state and corporate surveillance. We thus argue that encryption can act as a feminist tool to usurp patriarchal surveillance but must be used in combination with a holistic privacy framework. This framework may include care-motivated, community-centered, relational surveillance.
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 feedback loop into their behavioural policies. It centralizes breakages in these new data practices as the locus of analysis, showing how the feedback loop dissolves at various points or is never established due to shortcomings in the new technologies, regulatory barriers and aspects inherent to insurance logic itself, thereby undermining the policies’ aims; yet, the insurers’ experimentations cannot be simply framed as failures because they produce knowledge that can inform future practices. The present study illustrates the importance of examining how versions of dominant technological visions are reworked in local, field-specific practices. It shows that instead of producing ‘guaranteed outcomes’, these novel insurance operations raise new questions and uncertainties that can have harmful effects beyond the totalizing scenarios. Focusing on breakages challenges techno-deterministic perspectives, keeping the future open while enabling a more precise critique of the harms associated with these visions and their often-imperfect implementation.
How data on individuals are gathered, analyzed, and stored remains largely ungoverned at both domestic and global levels. We address the unique governance problem posed by digital data to provide a framework for understanding why data governance remains elusive. Data are easily transferable and replicable, making them a useful tool. But this characteristic creates massive governance problems for all of us who want to have some agency and choice over how (or if) our data are collected and used. Moreover, data are co‐created: individuals are the object from which data are culled by an interested party. Yet, any data point has a marginal value of close to zero and thus individuals have little bargaining power when it comes to negotiating with data collectors. Relatedly, data follow the rule of winner take all—the parties that have the most can leverage that data for greater accuracy and utility, leading to natural oligopolies. Finally, data's value lies in combination with proprietary algorithms that analyze and predict the patterns. Given these characteristics, private governance solutions are ineffective. Public solutions will also likely be insufficient. The imbalance in market power between platforms that collect data and individuals will be reproduced in the political sphere. We conclude that some form of collective data governance is required. We examine the challenges to the data governance by looking a public effort, the EU's General Data Protection Regulation, a private effort, Apple's “privacy nutrition labels” in their App Store, and a collective effort, the First Nations Information Governance Centre in Canada.
This study maps the evolution of research themes on datafication, analyzing trends, key authors, interdisciplinary collaborations, and emerging topics from 1994 to 2023. The analysis reveals a notable increase in publication volume, particularly from 2014 onwards, reflecting advancements in digital technologies and heightened interest in data-driven research. A significant surge occurred during the COVID-19 pandemic, with 26.10% of total publications in 2022 and 30.52% in 2023 alone. Thematic clusters identified through keyword mapping include Social Media and Privacy, Artificial Intelligence and Machine Learning, Human Dimensions, and Infrastructure and Trust, highlighting diverse research foci. Emerging discussions on data justice and inequality reflect growing attention to the ethical and socio-political implications of datafication. The study also examines the types of documents and subject areas, revealing the dominance of peer-reviewed journal articles (71.41%) and a strong representation of social sciences (46.93%), computer science (14.75%), and arts and humanities (11.57%). Interdisciplinary connections underscore the broad impact of datafication across technology, healthcare, education, and media studies. This research offers insights into the dynamic nature of datafication, pointing to the need for further interdisciplinary collaboration, especially in addressing societal and ethical concerns such as data governance and digital inequality. Future research directions should focus on the human dimensions of datafication, data literacy, and the development of robust data governance frameworks to mitigate potential inequalities and power imbalances in a rapidly data-driven world.
The paper examines Facebook’s ambition to extend into the entire web by focusing on social buttons and developing a medium-specific platform critique. It contextualises the rise of buttons and counters as metrics for user engagement and links them to different web economies. Facebook’s Like buttons enable multiple data flows between various actors, contributing to a simultaneous de- and re-centralisation of the web. They allow the instant transformation of user engagement into numbers on button counters, which can be traded and multiplied but also function as tracking devices. The increasing presence of buttons and associated social plugins on the web creates new forms of connectivity between websites, introducing an alternative fabric of the web. Contrary to Facebook’s claim to promote a more social experience of the web, this paper explores the implementation and technical infrastructure of such buttons to conceptualise them as part of a so-called ‘Like economy’.
This article explores the new modalities of visibility engendered by new media, with a focus on the social networking site Facebook. Influenced by Foucault’s writings on Panopticism – that is, the architectural structuring of visibility – this article argues for understanding the construction of visibility on Facebook through an architectural framework that pays particular attention to underlying software processes and algorithmic power. Through an analysis of EdgeRank, the algorithm structuring the flow of information and communication on Facebook’s ‘News Feed’, I argue that the regime of visibility constructed imposes a perceived ‘threat of invisibility’ on the part of the participatory subject. As a result, I reverse Foucault’s notion of surveillance as a form of permanent visibility, arguing that participatory subjectivity is not constituted through the imposed threat of an all-seeing vision machine, but by the constant possibility of disappearing and becoming obsolete.
Online content providers such as YouTube are carefully positioning themselves to users, clients, advertisers and policymakers, making strategic claims for what they do and do not do, and how their place in the information landscape should be understood. One term in particular, ‘platform’, reveals the contours of this discursive work. The term has been deployed in both their populist appeals and their marketing pitches, sometimes as technical ‘platforms’, sometimes as ‘platforms’ from which to speak, sometimes as ‘platforms’ of opportunity. Whatever tensions exist in serving all of these constituencies are carefully elided. The term also fits their efforts to shape information policy, where they seek protection for facilitating user expression, yet also seek limited liability for what those users say. As these providers become the curators of public discourse, we must examine the roles they aim to play, and the terms by which they hope to be judged.
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.
We show that easily accessible digital records of behavior, Facebook Likes, can be used to automatically and accurately predict a range of highly sensitive personal attributes including: sexual orientation, ethnicity, religious and political views, personality traits, intelligence, happiness, use of addictive substances, parental separation, age, and gender. The analysis presented is based on a dataset of over 58,000 volunteers who provided their Facebook Likes, detailed demographic profiles, and the results of several psychometric tests. The proposed model uses dimensionality reduction for preprocessing the Likes data, which are then entered into logistic/linear regression to predict individual psychodemographic profiles from Likes. The model correctly discriminates between homosexual and heterosexual men in 88% of cases, African Americans and Caucasian Americans in 95% of cases, and between Democrat and Republican in 85% of cases. For the personality trait "Openness," prediction accuracy is close to the test-retest accuracy of a standard personality test. We give examples of associations between attributes and Likes and discuss implications for online personalization and privacy.
A growing amount of content is published worldwide every day by millions of social media users. Most of this content is public, permanent, and searchable. At the same time, the number of studies proposing different techniques and methodologies to exploit this content as data for researchers in different disciplines is also growing. This article presents an up-to-date literature review that frames available studies using Facebook, Twitter, and YouTube as data sources, in the perspective of traditional approaches for social scientists: ethnographical, statistical, and computational. The aim is to offer an overview of strengths and weaknesses of different approaches in the context of the possibilities offered by the different platforms.
This article investigates how the new spirit of capitalism gets inscribed in the fabric of search algorithms by way of social practices. Drawing on the tradition of the social construction of technology (SCOT) and 17 qualitative expert interviews it discusses how search engines and their revenue models are negotiated and stabilized in a network of actors and interests, website providers and users first and foremost. It further shows how corporate search engines and their capitalist ideology are solidified in a socio-political context characterized by a techno-euphoric climate of innovation and a politics of privatization. This analysis provides a valuable contribution to contemporary search engine critique mainly focusing on search engines' business models and societal implications. It shows that a shift of perspective is needed from impacts search engines have on society towards social practices and power relations involved in the construction of search engines to renegotiate search engines and their algorithmic ideology in the future.
This paper provides fieldwork evidence, which solidifies an emerging view in literature, regarding the limitations of the panoptical metaphor in informing meaningfully and productively the analysis of contemporary surveillance and control. Our thesis is that the panopticon metaphor, which conceives of the organization as a bounded enclosure made up of divisible, observable and calculable spaces, is becoming less and less relevant in the age of contemporary surveillance technologies. Through a longitudinal socio-ethnographic study of the ramifications of surveillance ensuing from the implementation of a computerized knowledge management system (KMS) in a Parisian tax/law firm, our analysis points to the proliferation of lateral networks of surveillance having developed in the aftermath of implementation. In this complex and unstable constellation of rhizomatical controls, peers are involved in scrutinizing the validity of one another's work, irrespective of the office's hierarchies and official lines of specialization. As a result, games of visibility (exhibitionism), observation (voyeurism) and secrecy (hiding one's work from the KMS) abound in the office. One of our main conclusions is to emphasize the pertinence of apprehending control and surveillance from angles that take into account the ambiguities, complexities and unpredictability of human institutions, especially in digitalized environments.
This article investigates how the new spirit of capitalism gets inscribed in the fabric of search algorithms by way of social practices. Drawing on the tradition of the social construction of technology (SCOT) and 17 qualitative expert interviews it discusses how search engines and their revenue models are negotiated and stabilized in a network of actors and interests, website providers and users first and foremost. It further shows how corporate search engines and their capitalist ideology are solidified in a socio-political context characterized by a techno-euphoric climate of innovation and a politics of privatization. This analysis provides a valuable contribution to contemporary search engine critique mainly focusing on search engines' business models and societal implications. It shows that a shift of perspective is needed from impacts search engines have on society towards social practices and power relations involved in the construction of search engines to renegotiate search engines and their algorithmic ideology in the future.
Television broadcasters are beginning to combine social micro-blogging systems such as Twitter with television to create social video experiences around events. We looked at one such event, the first U.S. presidential debate in 2008, in conjunction with aggregated ratings of message sentiment from Twitter. We begin to develop an analytical methodol- ogy and visual representations that could help a journalist or public affairs person better understand the temporal dy- namics of sentiment in reaction to the debate video. We demonstrate visuals and metrics that can be used to detect sentiment pulse, anomalies in that pulse, and indications of controversial topics that can be used to inform the design of visual analytic systems for social media events.
Directed links in social media could represent anything from intimate friendships to common interests, or even a passion for breaking news or celebrity gossip. Such directed links determine the flow of information and hence indicate a user's influence on others—a concept that is crucial in sociology and viral marketing. In this paper, using a large amount of data collected from Twit- ter, we present an in-depth comparison of three mea- sures of influence: indegree, retweets, and mentions. Based on these measures, we investigate the dynam- ics of user influence across topics and time. We make several interesting observations. First, popular users who have high indegree are not necessarily influential in terms of spawning retweets or mentions. Second, most influential users can hold significant influence over a variety of topics. Third, influence is not gained spon- taneously or accidentally, but through concerted effort such as limiting tweets to a single topic. We believe that these findings provide new insights for viral marketing and suggest that topological measures such as indegree alone reveals very little about the influence of a user.
Twitter, a popular microblogging service, has received much attention recently. An important characteristic of Twitter is its real-time nature. For example, when an earthquake occurs, people make many Twitter posts (tweets) related to the earthquake, which enables detection of earthquake occurrence promptly, simply by observing the tweets. As described in this paper, we investigate the real-time inter- action of events such as earthquakes, in Twitter, and pro- pose an algorithm to monitor tweets and to detect a target event. To detect a target event, we devise a classifier of tweets based on features such as the keywords in a tweet, the number of words, and their context. Subsequently, we produce a probabilistic spatiotemporal model for the tar- get event that can find the center and the trajectory of the event location. We consider each Twitter user as a sensor and apply Kalman filtering and particle filtering, which are widely used for location estimation in ubiquitous/pervasive computing. The particle filter works better than other com- pared methods in estimating the centers of earthquakes and the trajectories of typhoons. As an application, we con- struct an earthquake reporting system in Japan. Because of the numerous earthquakes and the large number of Twit- ter users throughout the country, we can detect an earth- quake by monitoring tweets with high probability (96% of earthquakes of Japan Meteorological Agency (JMA) seis- mic intensity scale 3 or more are detected). Our system detects earthquakes promptly and sends e-mails to regis- tered users. Notification is delivered much faster than the announcements that are broadcast by the JMA.
Psychological personality has been shown to affect a variety of aspects: preferences for interaction styles in the digital world and for music genres, for example. Consequently, the design of personalized user interfaces and music recommender systems might benefit from understanding the relationship between personality and use of social media. Since there has not been a study between personality and use of Twitter at large, we set out to analyze the relationship between personality and different types of Twitter users, including popular users and influentials. For 335 users, we gather personality data, analyze it, and find that both popular users and influentials are extroverts and emotionally stable (low in the trait of Neuroticism). Interestingly, we also find that popular users are `imaginative' (high in Openness), while influentials tend to be `organized' (high in Conscientiousness). We then show a way of accurately predicting a user's personality simply based on three counts publicly available on profiles: following, followers, and listed counts. Knowing these three quantities about an active user, one can predict the user's five personality traits with a root-mean-squared error below 0.88 on a [1,5] scale. Based on these promising results, we argue that being able to predict user personality goes well beyond our initial goal of informing the design of new personalized applications as it, for example, expands current studies on privacy in social media.
In recent years, social media has become ubiquitous and important for social
networking and content sharing. And yet, the content that is generated from
these websites remains largely untapped. In this paper, we demonstrate how
social media content can be used to predict real-world outcomes. In particular,
we use the chatter from Twitter.com to forecast box-office revenues for movies.
We show that a simple model built from the rate at which tweets are created
about particular topics can outperform market-based predictors. We further
demonstrate how sentiments extracted from Twitter can be further utilized to
improve the forecasting power of social media.
The term Big Data is applied to data sets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time. Big data sizes are a constantly moving target, currently ranging from a few dozen terabytes to many petabytes of data in a single data set. This chapter addresses some of the theoretical and practical issues raised by the possibility of using massive amounts of social and cultural data in the humanities and social sciences. These observations are based on the author’s own experience working since 2007 with large cultural data sets at the Software Studies Initiative at the University of California, San Diego. The issues discussed include the differences between ‘deep data’ about a few people and ‘surface data’ about many people; getting access to transactional data; and the new “data analysis divide” between data experts and researchers without training in computer science.
This paper explores the tension between consumer power and surveillance through an analysis of group buying websites. These websites celebrate the power of the consumer generated through bulk purchases. Underlying the rhetoric about the autonomous consumer, however, is the systematic practice of buying, selling and reflecting consumer information. Through an examination of available promotional materials, websites and privacy policies, as well as interviews with representatives from group coupon companies, this article outlines a number of concerns surrounding the ways that digital surveillance techniques are being used, and have the potential to be used, to define consumer interests. The article argues that such practices are particularly problematic when they are couched in the rhetoric of consumer freedom and power. The article concludes by suggesting emerging industry trends, including industry consolidation and geolocation technology, which raise additional questions about how companies shape consumer behavior.
This article develops a critical alternative to the common equation between participatory culture and democratic communication and argues that power on online participatory platforms should be understood as the governance of semiotic open-endedness. This article argues that the concept of cultural expression cannot be understood solely by looking at users' cultural practices, but should be revisited to pay attention to the networked conditions that enable it. This involves tracing the governance of disparate processes such as protocols, software, linguistic processes, and cultural practices that make the production and circulation of meaning possible. Thus, communication on participatory platforms should be understood as the management of flows of meaning, that is, as the processes of codification of the informational, technical, cultural, and semiotic dynamics through which meanings are expressed. This makes it possible to understand the logics through which software platforms transform information into cultural signs and shape users' perceptions and agencies.
This article discusses methodological aspects of Big Data analyses with regard to their applicability and usefulness in digital media research. Based on a review of a diverse selection of literature on online methodology, consequences of using Big Data at different stages of the research process are examined. We argue that researchers need to consider whether the analysis of huge quantities of data is theoretically justified, given that it may be limited in validity and scope, and that small-scale analyses of communication content or user behavior can provide equally meaningful inferences when using proper sampling, measurement, and analytical procedures.
In a quiet London office, a software designer muses on the algorithms that will make possible the risk flags to be visualized on the screens of border guards from Heathrow to St Pancras International. There is, he says, ‘real time decision making’ – to detain, to deport, to secondarily question or search – but there is also the ‘offline team who run the analytics and work out the best set of rules’. Writing the code that will decide the association rules between items of data, prosaic and mundane – flight route, payment type, passport – the analysts derive a novel preemptive security measure. This paper proposes the analytic of the data derivative – a visualized risk flag or score drawn from an amalgam of disaggregated fragments of data, inferred from across the gaps between data and projected onto an array of uncertain futures. In contrast to disciplinary and enclosed techniques of collecting data to govern population, the data derivative functions via ‘differential curves of normality’, imagining a range of potential futures through the association rule, thus ‘opening up to let things happen’ (Foucault 2007). In some senses akin to the risk orientation of the financial derivative, itself indifferent to actual underlying people, places or events by virtue of modulated norms, the contemporary security derivative is not centred on who we are, nor even on what our data say about us, but on what can be imagined and inferred about who we might be – on our very proclivities and potentialities.
Marketing and web analytic companies have implemented sophisticated algorithms to observe, analyze, and identify users through large surveillance networks online. These computer algorithms have the capacity to infer categories of identity upon users based largely on their web-surfing habits. In this article I will first discuss the conceptual and theoretical work around code, outlining its use in an analysis of online categorization practices. The article will then approach the function of code at the level of the category, arguing that an analysis of coded computer algorithms enables a supplement to Foucauldian thinking around biopolitics and biopower, of what I call soft biopower and soft biopolitics. These new conceptual devices allow us to better understand the workings of biopower at the level of the category, of using computer code, statistics and surveillance to construct categories within populations according to users’ surveilled internet history. Finally, the article will think through the nuanced ways that algorithmic inference works as a mode of control, of processes of identification that structure and regulate our lives online within the context of online marketing and algorithmic categorization.
Social media are popular stages for self-expression, communication and self-promotion. Rather than facilitating online identity formation, they are sites of struggle between users, employers and platform owners to control online identities – a struggle played out at the level of the interface. This article offers a comparative interface analysis between Facebook and LinkedIn. While Facebook is particularly focused on facilitating personal self-presentation, LinkedIn’s interface caters towards the need for professional self-promotion. And yet, both platforms deploy similar principles of connectivity and narrative – strategies that can be succinctly revealed in recent interface changes. These changing digital architectures form the necessary backdrop for asking critical questions about online self-presentation: How are public identities shaped through platform interfaces? How do these features enable and constrain the sculpting of personal and professional persona? And what are the consequences of imposed connectivity and narrative uniformity on people’s online identities?
This article focuses upon the concept of ‘affective economics’ arguing that it should be expanded to include a consideration of emerging forms of data-mining including ‘sentiment analysis’ and ‘predictive analytics’. Sentiment analysis in particular seeks to manipulate consumer behaviour by gathering data about emotional responses and conducting controlled experiments on consumers. Any consideration of affective economics should include the ways in which marketers seek to manage consumers through the collection not just of demographic information, but of extensive real-time databases of their online behaviour and conversations.
In this article, we demonstrate the value of long-term query logs. Most work on query logs to date considers only short-term (within-session) query information. In contrast, we show that long-term query logs can be used to learn about the world we live in. There are many applications of this that lead not only to improving the search engine for its users, but also potentially to advances in other disciplines such as medicine, sociology, economics, and more. In this article, we will show how long-term query logs can be used for these purposes, and that their potential is severely reduced if the logs are limited to short time horizons. We show that query effects are long-lasting, provide valuable information, and might be used to automatically make medical discoveries, build concept hierarchies, and generally learn about the sociological behavior of users. We believe these applications are only the beginning of what can be done with the information contained in long-term query logs, and see this work as a step toward unlocking their potential.
For the period of the spread of pandemic H1N1 influenza in New Zealand during 2009, we compared results from Google Flu Trends with data from existing surveillance systems. The patterns from Google Flu Trends were closely aligned with (peaking a week before and a week after) two independent national surveillance systems for influenza-like illness (ILI) cases. It was much less congruent with (delayed by three weeks) data from ILI-related calls to a national free-phone Healthline and with media coverage of pandemic influenza. Some patterns were unique to Google Flu Trends and may not have reflected the actual ILI burden in the community. Overall, Google Flu Trends appears to provide a useful free surveillance system but it should probably be seen as supplementary rather than as an alternative.
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Big Data in Small Hands
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