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Technology does not exist in a vacuum. In the same way that a plant needs water and nourishment to grow, technology needs people and process to thrive and succeed. Culture (i.e., people and process) is integral and critical to the success of any new technology deployment or implementation. Big data is not just a technology phenomenon. It has a cultural dimension. It's vitally important to remember that most people have not considered the immense difference between a world seen through the lens of a traditional relational database system and a world seen through the lens of a Hadoop Distributed File System.This paper broadly describes the cultural challenges that accompany efforts to create and sustain big data initiatives in an evolving world whose data management processes are rooted firmly in traditional data warehouse architectures.
“One of the most exciting developments from the world of ideas in decades, presented with panache by two frighteningly brilliant, endearingly unpretentious, and endlessly creative young scientists.” – Steven Pinker, author of The Better Angels of Our Nature Our society has gone from writing snippets of information by hand to generating a vast flood of 1s and 0s that record almost every aspect of our lives: who we know, what we do, where we go, what we buy, and who we love. This year, the world will generate 5 zettabytes of data. (That’s a five with twenty-one zeros after it.) Big data is revolutionizing the sciences, transforming the humanities, and renegotiating the boundary between industry and the ivory tower. What is emerging is a new way of understanding our world, our past, and possibly, our future. In Uncharted, Erez Aiden and Jean-Baptiste Michel tell the story of how they tapped into this sea of information to create a new kind of telescope: a tool that, instead of uncovering the motions of distant stars, charts trends in human history across the centuries. By teaming up with Google, they were able to analyze the text of millions of books. The result was a new field of research and a scientific tool, the Google Ngram Viewer, so groundbreaking that its public release made the front page of The New York Times, The Wall Street Journal, and The Boston Globe, and so addictive that Mother Jones called it “the greatest timewaster in the history of the internet.” Using this scope, Aiden and Michel—and millions of users worldwide—are beginning to see answers to a dizzying array of once intractable questions. How quickly does technology spread? Do we talk less about God today? When did people start “having sex” instead of “making love”? At what age do the most famous people become famous? How fast does grammar change? Which writers had their works most effectively censored by the Nazis? When did the spelling “donut” start replacing the venerable “doughnut”? Can we predict the future of human history? Who is better known—Bill Clinton or the rutabaga? All over the world, new scopes are popping up, using big data to quantify the human experience at the grandest scales possible. Yet dangers lurk in this ocean of 1s and 0s—threats to privacy and the specter of ubiquitous government surveillance. Aiden and Michel take readers on a voyage through these uncharted waters.
"I shall consider human actions and appetites just as if it were a question of lines, planes, and bodies." -Spinoza, in Ethics In her first inquiry toward decelerationist aesthetics, Katherine Behar explores the rise of two "big deal" contemporary phenomena, big data and obesity. In both, scale rearticulates the human as a diffuse informational pattern, causing important shifts in political form as well as aesthetic form. Bigness redraws relationships between the singular and the collective. Understood as informational patterns, collectives can be radically inclusive, even incorporating nonhumans. As a result, the political subject is slowly becoming a new object. This social and informational body belongs to no single individual, but is shared in solidarity with something "bigger than you." In decelerationist aesthetics, the aesthetic properties, proclivities, and performances of objects come to defy the accelerationist imperative to be nimbly individuated. Decelerationist aesthetics rejects atomistic, liberal, humanist subjects; this unit of self is too consonant with capitalist relations and functions. Instead, decelerationist aesthetics favors transhuman sociality embodied in particulate, mattered objects; the aesthetic form of such objects resists capitalist speed and immediacy by taking back and taking up space and time. In just this way, big data calls into question the conventions by which humans are defined as discrete entities, and individual scales of agency are made to form central binding pillars of social existence through which bodies are drawn into relations of power and pathos.
In a data-driven world, anything can be data. As the techniques and scale of data analysis advance, the need for a response from rhetoric and composition grows ever more pronounced. It is increasingly possible to examine thousands of documents and peer-review comments, labor-hours, and citation networks in composition courses and beyond. Composition and Big Data brings together a range of scholars, teachers, and administrators already working with big-data methods and datasets to kickstart a collective reckoning with the role that algorithmic and computational approaches can, or should, play in research and teaching in the field. Their work takes place in various contexts, including programmatic assessment, first-year pedagogy, stylistics, and learning transfer across the curriculum. From ethical reflections to database design, from corpus linguistics to quantitative autoethnography, these chapters implement and interpret the drive toward data in diverse ways.
This open access book explores the challenges society faces with big data, through the lens of culture rather than social, political or economic trends, as demonstrated in the words we use, the values that underpin our interactions, and the biases and assumptions that drive us. Focusing on areas such as data and language, data and sensemaking, data and power, data and invisibility, and big data aggregation, it demonstrates that humanities research, focussing on cultural rather than social, political or economic frames of reference for viewing technology, resists mass datafication for a reason, and that those very reasons can be instructive for the critical observation of big data research and innovation. The eBook editions of this book are available open access under a CC BY-NC-ND 4.0 licence on bloomsburycollections.com. Open access was funded by Trinity College Dublin, DARIAH-EU and the European Commission.
Social scientists seek to develop systematic ways to understand how people make meaning and how the meanings they make shape them and the world in which they live. But how do we measure such processes? Measuring Culture is an essential point of entry for both those new to the field and those who are deeply immersed in the measurement of meaning. Written collectively by a team of leading qualitative and quantitative sociologists of culture, the book considers three common subjects of measurement—people, objects, and relationships—and then discusses how to pivot effectively between subjects and methods. Measuring Culture takes the reader on a tour of the state of the art in measuring meaning, from discussions of neuroscience to computational social science. It provides both the definitive introduction to the sociological literature on culture as well as a critical set of case studies for methods courses across the social sciences.
A exploration of the latest trend in technology and the impact it will have on the economy, science, and society at large.
A book at the intersection of data science and media studies, presenting concepts and methods for computational analysis of cultural data. How can we see a billion images? What analytical methods can we bring to bear on the astonishing scale of digital culture--the billions of photographs shared on social media every day, the hundreds of millions of songs created by twenty million musicians on Soundcloud, the content of four billion Pinterest boards? In Cultural Analytics, Lev Manovich presents concepts and methods for computational analysis of cultural data. Drawing on more than a decade of research and projects from his own lab, Manovich offers a gentle, nontechnical introduction to the core ideas of data analytics and discusses the ways that our society uses data and algorithms.
As digital technologies occupy a more central role in working and everyday human life, individual and social realities are increasingly constructed and communicated through digital objects, which are progressively replacing and representing physical objects. They are even shaping new forms of virtual reality. This growing digital transformation coupled with technological evolution and the development of computer computation is shaping a cyber society whose working mechanisms are grounded upon the production, deployment, and exploitation of big data. In the arts and humanities, however, the notion of big data is still in its embryonic stage, and only in the last few years, have arts and cultural organizations and institutions, artists, and humanists started to investigate, explore, and experiment with the deployment and exploitation of big data as well as understand the possible forms of collaborations based on it. Big Data in the Arts and Humanities: Theory and Practice explores the meaning, properties, and applications of big data. This book examines therelevance of big data to the arts and humanities, digital humanities, and management of big data with and for the arts and humanities. It explores the reasons and opportunities for the arts and humanities to embrace the big data revolution. The book also delineates managerial implications to successfully shape a mutually beneficial partnership between the arts and humanities and the big data- and computational digital-based sciences. Big data and arts and humanities can be likened to the rational and emotional aspects of the human mind. This book attempts to integrate these two aspects of human thought to advance decision-making and to enhance the expression of the best of human life.
Algorithmic Culture: How Big Data and Artificial Intelligence are Transforming Everyday Life explores the complex ways in which algorithms and big data, or algorithmic culture, are simultaneously reshaping everyday culture while perpetuating inequality and intersectional discrimination. Contributors situate issues of humanity, identity, and culture in relation to free will, surveillance, capitalism, neoliberalism, consumerism, solipsism, and creativity, offering a critique of the myriad constraints enacted by algorithms. This book argues that consumers are undergoing an ontological overhaul due to the enhanced manipulability and increasingly mandatory nature of algorithms in the market, while also positing that algorithms may help navigate through chaos that is intrinsically present in the market democracy. Ultimately, Algorithmic Culture calls attention to the present-day cultural landscape as a whole as it has been reconfigured and re-presented by algorithms.