scholarly journals THE DEAD SPEAK: BIG DATA AND DIGITALLY MEDIATED DEATH

2020 ◽  
Author(s):  
Justin Grandinetti ◽  
Tyler DeAtley ◽  
Jeffery Bruinsma
Keyword(s):  
Big Data ◽  
Digital Media ◽  
Streaming Media ◽  
Economic Value ◽  
Digital Data ◽  
Data Streaming ◽  
Loved One ◽  
User Data ◽  
The Dead ◽  
The Individual

In the following panel, we add to scholarly challenges regarding the binary distinction between life and death by examining new strategies of making productive the data of the dead. Digital media and tactics of big data collection, storage, and processing blur the boundaries of human lifecycles, allowing the individual to exist as a productive part of sociotechnical apparatuses long after their corporeal demise. Specifically, our presentations on digital data and death focus on the topics of subjectivation, consent and privacy, and commodification. Reanimator: Haunted Data, Streaming Media, and Subjectivity examines the process of subjectivation taking into account the haunted data and digital afterlives of streaming media. Here, the living and bounded subject is challenged by compositions of big data, platforms, infrastructures, and algorithms that offer the possibility of a productive sociotechnical economic subject unbounded from the human body. Grief by the Byte: Constructions of Data Consent, Privacy, and Stability in Griefbots interrogates the data practices and ethics related to the creation of chatbots from the data of deceased individuals. While “griefbots” are framed as helpful to those grieving a lost loved one, there remain questions about consent and privacy that accompany these interactions. Finally, What is Dead May Never Die: The Commodification of Death in Social Media studies how user data maintains economic value after death via networks designed to surveil, collect, and commodify the immaterial labor of the dead. These practices enable a possible economic future largely influenced by the data of the dead.

scholarly journals Digital Transformation, Big Data and Research Landscape in Digital Communication

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Imam Syafganti
Keyword(s):  
Big Data ◽  
Digital Media ◽  
Research Method ◽  
Social Research ◽  
Digital Data ◽  
Communication Research ◽  
Research Directions ◽  
Critical Issues ◽  
Data Source ◽  
Media Research

The digitization of communications technology has led to an intense interaction between human and digital-based technology. A large number of digital data traces produced by humans as a result of that activity. Such data is commonly referred to Big Data. The availability of Big Data as a digital data source in turn, opens opportunities for communication scientists to be able to use that data to get the patterns and trends of human activities that have been done through social research. It is necessary to understand the basic concept of the Big Data, using appropriate tools and adequate access to the data, and appropriate research method in order to be able to conduct research by using such digital data. This paper aims to describe the potential of Big Data for the purposes of communication research, the use of appropriate tools, techniques and methods and to identify potential research directions in the digital realm. Some limitations and critical issues related to the research validity, population and sample, as well as ethics in digital media research method were also discussed.

scholarly journals THE AFTERLIVES OF MEMORIAL MATERIALS: DATA, HOAX, BOT

2019 ◽  
Vol 2019 ◽  
Author(s):  
Bjorn Nansen ◽  
Larissa Hjorth ◽  
Stacey Pitsillides ◽  
Hannah Gould
Keyword(s):  
Digital Media ◽  
New Technologies ◽  
Death And Dying ◽  
Personal Data ◽  
Digital Data ◽  
Research Network ◽  
The Internet ◽  
Diverse Field ◽  
De Vries ◽  
The Dead

The study of death online has often intersected with questions of trust, though such questions have evolved over time to not only include relations of trust between individuals and within online communities, but also issues of trust emerging through entanglements and interactions with the afterlives of memorial materials. Papers in this panel attend to the growing significance of the afterlives of digital data, the circulation of fake deaths, the care attached to memorial bots, and the intersection of robots and funerals. Over the last twenty years the study of death online developed into a diverse field of enquiry. Early literature addressed the emergence of webpages created as online memorials and focused on their function to commemorate individuals by extending memorial artefacts from physical to digital spaces for the bereaved to gather (De Vries and Rutherford, 2004; Roberts, 2004; Roberts and Vidal, 2000; Veale, 2004). The emergence of platforms for social networking in the mid-2000s broadened the scope of research to include increasingly knotted questions around the ethics, politics and economics of death online. Scholars began investigating issues like the performance of public mourning, our obligations to and management of the digital remains of the deceased, the affordances of platforms for sharing or trolling the dead, the extraction of value from the data of the deceased, and the ontology of entities that digitally persist (e.g. Brubaker and Callison-Burch, 2016; Gibbs et al., 2015; Karppi, 2013; Marwick and Ellison, 2012; Phillips, 2011; Stokes, 2012). Scaffolding this scholarship are a number of research networks, including the Death Online Research Network and the DeathTech Research Network, who encourage international collaboration and conversation around the study of death and digital media, including supporting this AoIR panel. This panel contributes to the growing field of research on death and digital media, and in particular poses challenges to focus on the commemoration of humans to encompass broader issues around the data and materiality of digital death. Digital residues of the deceased persist within and circulate through online spaces, enrolling users into new configurations of posthumous dependence on platforms, whilst at the same time digital afterlives now intersect with new technologies to create emergent forms of agency such as chatbots and robots that extend beyond the human, demanding to be considered within the sphere of digital memorialisation. Questions of trust emerge in this panel through various kinds of relationality formed with and through digital remains. These extend from relations of trust in the digital legacies now archived within platform architectures and how we might curate conversations differently around our personal data; to the breaking of trust in the internet when creating or sharing a hoax death; to the trust involved in making and caring for a posthumous bot; to the trust granted to robots to perform funerary rites. It is anticipated that this panel will not only appeal to scholars interested in the area of death and digital media, but also engage with broader scholarly communities in which questions of death now arise in larger debates around data, materiality, and governance on and of the internet. References Brubaker, J. R. and Callison-Burch, V. (2016) Legacy Contact: Designing and Implementing Post-mortem Stewardship at Facebook. Paper presented at CHI Workshop on Human Factors in Computer Systems, San Jose California. de Vries, B. and Rutherford, J. (2004) Memorializing Loved Ones on the World Wide Web. Omega: Journal of Death and Dying, 49(1), 5-26. Gibbs, M., Meese, J., Arnold, M., Nansen, B., and Carter, M. (2015) #Funeral and Instagram: Death, Social Media and Platform Vernacular. Information Communication and Society, 18(3): 255-268. Karppi, T. (2013) Death proof: on the biopolitics and noopolitics of memorializing dead Facebook users. Culture Machine, 14, 1-20. Marwick, A. and Ellison, N. (2012) “There Isn’t Wifi in Heaven!” Negotiating Visibility on Facebook Memorial Pages. Journal of Broadcasting and Electronic Media 56(3), 378–400. Phillips, W. (2011) LOLing at Tragedy: Facebook Trolls, Memorial Pages and Resistance to Grief Online. First Monday 16(12). Retrieved from http://firstmonday.org Roberts, P. (2004) The Living and the Dead: Community in the Virtual Cemetery. Omega: Journal of Death and Dying, 49(1), 57-76. Stokes, P. (2012) Ghosts in the Machine: Do the Dead Live on in Facebook? Philosophy and Technology, 25(3), 363-379. Veale, K. (2004) Online Memorialisation: The Web as a Collective Memorial Landscape For Remembering The Dead. The Fibreculture Journal, 3. Retrieved from http://three.fibreculturejournal.org  

scholarly journals Climate risks, digital media, and big data: following communication trails to investigate urban communities' resilience

2019 ◽  
Vol 19 (7) ◽  
pp. 1485-1498 ◽  
Author(s):  
Rosa Vicari ◽  
Ioulia Tchiguirinskaia ◽  
Bruno Tisserand ◽  
Daniel Schertzer
Keyword(s):  
Big Data ◽  
Digital Media ◽  
Urban Areas ◽  
Visual Observation ◽  
Digital Data ◽  
Urban Resilience ◽  
Seine River ◽  
Incidence Graph ◽  
River Flood ◽  
Key Terms

Abstract. Today, when extreme weather affects an urban area, huge numbers of digital data are spontaneously produced by the population on the Internet. These “digital trails” can provide insight into the interactions existing between climate-related risks and the social perception of these risks. According to this research “big data” exploration techniques can be exploited to monitor these interactions and their effect on urban resilience. The experiments presented in this paper show that digital research can amplify key issues covered by digital media and identify the stakeholders that can influence the debate, and therefore the community's attitudes towards an issue. Three corpora of Web communication data have been extracted: press articles covering the June 2016 Seine River flood, press articles covering the October 2015 Alpes-Maritimes flood, and tweets on the 2016 Seine River flood. The analysis of these datasets involved an iteration between manual and automated extraction of hundreds of key terms, aggregated analysis of publication incidence and key term incidence, graph representations based on measures of semantic proximity (conditional distance) between key terms, automated visualisation of clusters through Louvain modularity, visual observation of the graph, and quantitative analysis of its nodes and edges. Through this analysis we detected topics and actors that characterise each press dataset, as well as frequent co-occurrences and clusters of topics and actors. Profiling of social media users gave us insights into who could influence opinions on Twitter. Through a comparison of the three datasets, it was also possible to observe how some patterns change over time, in different urban areas and in different digital media contexts.

scholarly journals Climate risks, digital media, and big data: following communication trails to investigate urban communities' resilience

2018 ◽  
Author(s):  
Rosa Vicari ◽  
Ioulia Tchiguirinskaia ◽  
Daniel Schertzer
Keyword(s):  
Big Data ◽  
Digital Media ◽  
Urban Areas ◽  
Visual Observation ◽  
Digital Data ◽  
Urban Resilience ◽  
Seine River ◽  
Climate Risks ◽  
River Flood ◽  
Key Terms

Abstract. Nowadays, when extreme weather affects an urban area, huge amounts of digital data are spontaneously produced by the population on the Internet. These digital trails can provide an insight on the interactions existing between climate related risks and the social perception of these risks. According to this research big data exploration techniques can be exploited to monitor these interactions and their effect on urban resilience. The experiments presented in this paper show that digital research can bring out the most central issues in the digital media, identify the stakeholders that have the capacity to influence the debate and, therefore, the community attitudes towards an issue. Three corpora of Web communication data have been extracted: press news covering the June 2016 Seine River flood; press news covering the October 2015 Alpes-Maritimes flood; tweets on the 2016 Seine River flood. The analysis of these datasets involves an iteration between manual and automated extraction of hundreds of key terms, network representations based on key terms co-occurrences, automated cluster visualisation based on adjacency matrix, and profiling of social media users. Visual observation of the network coupled to quantitative analysis of its nodes and edges allow obtaining an in-depth understanding of the most prominent topics and actors, as well as of the connections and clusters that these topics and actors tend to form in the journalistic sphere. Through a comparison of the three datasets, it is also possible to observe how these patterns change over time, in different urban areas and in different digital media contexts.

How algorithms see their audience: media epistemes and the changing conception of the individual

2019 ◽  
Vol 41 (8) ◽  
pp. 1176-1191 ◽  
Author(s):  
Eran Fisher ◽  
Yoav Mehozay
Keyword(s):  
Big Data ◽  
Mass Media ◽  
Digital Media ◽  
Empirical Research ◽  
Social Theory ◽  
The Self ◽  
Human Beings ◽  
The Media ◽  
The Individual ◽  
The Way

The rise of digital media has witnessed a paradigmatic shift in the way that media outlets conceptualize and classify their audience. Whereas during the era of mass media, ‘seeing’ the audience was based on a scientific episteme combining social theory and empirical research, with digital media ‘seeing’ the audience has come to be dominated by a new episteme, based on big data and algorithms. This article argues that the algorithmic episteme does not see the audience more accurately, but differently. Whereas the scientific episteme upheld an ascriptive conception which assigned individuals to a particular social category, the algorithmic episteme assumes a performative individual, based on behavioral data, sidestepping any need for a theory of the self. Since the way in which the media see their audience is constitutive, we suggest that the algorithmic episteme represents a new way to think about human beings.

scholarly journals Práticas linguísticas em Big Data / Practical language in Big Data

2017 ◽  
Vol 10 (1) ◽  
pp. 31-52
Author(s):  
Vinícius Vargas Vieira dos Santos
Keyword(s):  
Big Data ◽  
Web 2.0 ◽  
Digital Media ◽  
Mass Communication ◽  
High Speed ◽  
Digital Data ◽  
Current Configuration ◽  
Time Space ◽  
Technology Services ◽  
High Speed Data

RESUMO: O presente artigo objetiva tratar possíveis relações entre novas mídias digitais e certos aspectos conceituais da linguagem, como significado e performatividade. Big data é o termo que se refere ao acúmulo de dados digitais que caracterizou as mídias de comunicação em massa nas duas últimas décadas e está diretamente relacionado à atual configuração da plataforma de serviços de tecnologia Web 2.0. As escalas de desmedido volume e variedade de dados digitais e altos índices de velocidade que caracterizam o Big data modificam as paisagens de contexto social, provocando, consequentemente, atualizações nas escalas da linguagem. Afinal, contextos em ambientes virtuais entram em colapso, pois ao assumir as próprias características do meio, revelam-se superdiversos, simultâneos, fragmentados, não estruturados, ausentes de marcadores familiares, excedendo escalas tradicionais de tempo, espaço e alcance social. ABSTRACT: This article aims at assimilating possible relationships among new digital media and certain conceptual aspects of language, such as meaning and performativity. Big data is a term that refers to digital data accumulation that characterized the mass communication media in the last two decades and it is directly related to the current configuration of Web 2.0 technology services platform. The scales of excessive quantity and variety of digital data and high speed data which characterize the Big data change the social context of landscapes, causing consequently updates on the scales of language. After all, contexts in virtual environments collapse, because they assume the characteristics of the environment, that is, they show themselves as superdiverse, simultaneous, fragmented, unstructured, missing family markers, exceeding traditional scales of time, space and social reach.

scholarly journals Beyond Privacy: Protecting Data Interests in the Age of Artificial Intelligence

2021 ◽  
Vol 3 (1) ◽  
pp. 96-108
Author(s):  
Matt Bartlett
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Economic Value ◽  
Social Dimension ◽  
Data Driven ◽  
Data Control ◽  
The Social ◽  
User Data ◽  
General Data

Serious challenges are raised by the way in which technology companies like Facebook and Google harvest and process user data. Companies in the modern data economy mine troves of data with sophisticated algorithms to produce valuable behavioural predictions. These data-driven predictions provide companies with a powerful capacity to influence and manipulate users, and these risks are increasing with the explosive growth of ‘Big Data’ and artificial intelligence machine learning. This article analyses the extent to which these challenges are met by existing regimes such as Australia and New Zealand’s respective privacy acts and the European Union’s General Data Protection Regime. While these laws protect certain privacy interests, I argue that users have a broader set of interests in their data meriting protection. I explore three of these novel interests, including the social dimension of data, control and access to predictions mined from data and the economic value of data. This article shows how existing frameworks fail to recognise or protect these novel interests. In light of this failure, lawmakers urgently need to frame new legal regimes to protect against the worst excesses of the data economy.

Big Data and Communication Research

2016 ◽  
Author(s):  
Ralph Schroeder
Keyword(s):  
Big Data ◽  
Digital Media ◽  
Interpersonal Communication ◽  
Political Communication ◽  
Mobile Communication ◽  
Analytical Framework ◽  
Digital Data ◽  
Communication Research ◽  
Or Groups ◽  
Filter Bubble

Communication research has recently had an influx of groundbreaking findings based on big data. Examples include not only analyses of Twitter, Wikipedia, and Facebook, but also of search engine and smartphone uses. These can be put together under the label “digital media.” This article reviews some of the main findings of this research, emphasizing how big data findings contribute to existing theories and findings in communication research, which have so far been lacking. To do this, an analytical framework will be developed concerning the sources of digital data and how they relate to the pertinent media. This framework shows how data sources support making statements about the relation between digital media and social change. It is also possible to distinguish between a number of subfields that big data studies contribute to, including political communication, social network analysis, and mobile communication. One of the major challenges is that most of this research does not fall into the two main traditions in the study of communication, mass and interpersonal communication. This is readily apparent for media like Twitter and Facebook, where messages are often distributed in groups rather than broadcast or shared between only two people. This challenge also applies, for example, to the use of search engines, where the technology can tailor results to particular users or groups (this has been labeled the “filter bubble” effect). The framework is used to locate and integrate big data findings in the landscape of communication research, and thus to provide a guide to this emerging area.

A Review on Big Data Analytics in Business

2018 ◽  
pp. 210-214
Author(s):  
Manbir Sandhu ◽  
Purnima, Anuradha Saini
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Smart Devices ◽  
Data Streaming ◽  
Huge Amount ◽  
Business Units ◽  
Enormous Amount ◽  
Tools And Techniques ◽  
Heath Care ◽  
Data Analytic

Big data is a fast-growing technology that has the scope to mine huge amount of data to be used in various analytic applications. With large amount of data streaming in from a myriad of sources: social media, online transactions and ubiquity of smart devices, Big Data is practically garnering attention across all stakeholders from academics, banking, government, heath care, manufacturing and retail. Big Data refers to an enormous amount of data generated from disparate sources along with data analytic techniques to examine this voluminous data for predictive trends and patterns, to exploit new growth opportunities, to gain insight, to make informed decisions and optimize processes. Data-driven decision making is the essence of business establishments. The explosive growth of data is steering the business units to tap the potential of Big Data to achieve fueling growth and to achieve a cutting edge over their competitors. The overwhelming generation of data brings with it, its share of concerns. This paper discusses the concept of Big Data, its characteristics, the tools and techniques deployed by organizations to harness the power of Big Data and the daunting issues that hinder the adoption of Business Intelligence in Big Data strategies in organizations.

Big data investments in knowledge and non-knowledge intensive firms: what the market tells us

2017 ◽  
Vol 21 (3) ◽  
pp. 623-639 ◽  
Author(s):  
Tingting Zhang ◽  
William Yu Chung Wang ◽  
David J. Pauleen
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Stock Market ◽  
Value Chain ◽  
Stock Prices ◽  
Economic Value ◽  
Information Value ◽  
Publicly Traded ◽  
Content Type ◽  
Knowledge Intensive

Purpose This paper aims to investigate the value of big data investments by examining the market reaction to company announcements of big data investments and tests the effect for firms that are either knowledge intensive or not. Design/methodology/approach This study is based on an event study using data from two stock markets in China. Findings The stock market sees an overall index increase in stock prices when announcements of big data investments are revealed by grouping all the listed firms included in the sample. Increased stock prices are also the case for non-knowledge intensive firms. However, the stock market does not seem to react to big data investment announcements by testing the knowledge intensive firms along. Research limitations/implications This study contributes to the literature on assessing the economic value of big data investments from the perspective of big data information value chain by taking an unexpected change in stock price as the measure of the financial performance of the investment and by comparing market reactions between knowledge intensive firms and non-knowledge intensive firms. Findings of this study can be used to refine practitioners’ understanding of the economic value of big data investments to different firms and provide guidance to their future investments in knowledge management to maximize the benefits along the big data information value chain. However, findings of study should be interpreted carefully when applying them to companies that are not publicly traded on the stock market or listed on other financial markets. Originality/value Based on the concept of big data information value chain, this study advances research on the economic value of big data investments. Taking the perspective of stock market investors, this study investigates how the stock market reacts to big data investments by comparing the reactions to knowledge-intensive firms and non-knowledge-intensive firms. The results may be particularly interesting to those publicly traded companies that have not previously invested in knowledge management systems. The findings imply that stock investors tend to believe that big data investment could possibly increase the future returns for non-knowledge-intensive firms.

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