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This book explores the big data evolution by interrogating the notion that big data is a disruptive innovation that appears to be challenging existing epistemologies in the humanities and social sciences. Exploring various (controversial) facets of big data such as ethics, data power, and data justice, the book attempts to clarify the trajectory of the epistemology of (big) data-driven science in the humanities and social sciences.
This book explores the big data evolution by interrogating the notion that big data is a disruptive innovation that appears to be challenging existing epistemologies in the humanities and social sciences. Exploring various (controversial) facets of big data such as ethics, data power, and data justice, the book attempts to clarify the trajectory of the epistemology of (big) data-driven science in the humanities and social sciences.
This edited volume focuses on big data implications for computational social science and humanities from management to usage. The first part of the book covers geographic data, text corpus data, and social media data, and exemplifies their concrete applications in a wide range of fields including anthropology, economics, finance, geography, history, linguistics, political science, psychology, public health, and mass communications. The second part of the book provides a panoramic view of the development of big data in the fields of computational social sciences and humanities. The following questions are addressed: why is there a need for novel data governance for this new type of data?, why is big data important for social scientists?, and how will it revolutionize the way social scientists conduct research? With the advent of the information age and technologies such as Web 2.0, ubiquitous computing, wearable devices, and the Internet of Things, digital society has fundamentally changed what we now know as "data", the very use of this data, and what we now call "knowledge". Big data has become the standard in social sciences, and has made these sciences more computational. Big Data in Computational Social Science and Humanities will appeal to graduate students and researchers working in the many subfields of the social sciences and humanities.
A new era of innovation is enabled by the integration of social sciences and information systems research. In this context, the adoption of Big Data and analytics technology brings new insight to the social sciences. It also delivers new, flexible responses to crucial social problems and challenges. We are proud to deliver this edited volume on the social impact of big data research. It is one of the first initiatives worldwide analyzing of the impact of this kind of research on individuals and social issues. The organization of the relevant debate is arranged around three pillars: Section A: Big Data Research for Social Impact: • Big Data and Their Social Impact; • (Smart) Citizens from Data Providers to Decision-Makers; • Towards Sustainable Development of Online Communities; • Sentiment from Online Social Networks; • Big Data for Innovation. Section B. Techniques and Methods for Big Data driven research for Social Sciences and Social Impact: • Opinion Mining on Social Media; • Sentiment Analysis of User Preferences; • Sustainable Urban Communities; • Gender Based Check-In Behavior by Using Social Media Big Data; • Web Data-Mining Techniques; • Semantic Network Analysis of Legacy News Media Perception. Section C. Big Data Research Strategies: • Skill Needs for Early Career Researchers—A Text Mining Approach; • Pattern Recognition through Bibliometric Analysis; • Assessing an Organization’s Readiness to Adopt Big Data; • Machine Learning for Predicting Performance; • Analyzing Online Reviews Using Text Mining; • Context–Problem Network and Quantitative Method of Patent Analysis. Complementary social and technological factors including: • Big Social Networks on Sustainable Economic Development; Business Intelligence.
An examination of the uses of data within a changing knowledge infrastructure, offering analysis and case studies from the sciences, social sciences, and humanities. “Big Data” is on the covers of Science, Nature, the Economist, and Wired magazines, on the front pages of the Wall Street Journal and the New York Times. But despite the media hyperbole, as Christine Borgman points out in this examination of data and scholarly research, having the right data is usually better than having more data; little data can be just as valuable as big data. In many cases, there are no data—because relevant data don't exist, cannot be found, or are not available. Moreover, data sharing is difficult, incentives to do so are minimal, and data practices vary widely across disciplines. Borgman, an often-cited authority on scholarly communication, argues that data have no value or meaning in isolation; they exist within a knowledge infrastructure—an ecology of people, practices, technologies, institutions, material objects, and relationships. After laying out the premises of her investigation—six “provocations” meant to inspire discussion about the uses of data in scholarship—Borgman offers case studies of data practices in the sciences, the social sciences, and the humanities, and then considers the implications of her findings for scholarly practice and research policy. To manage and exploit data over the long term, Borgman argues, requires massive investment in knowledge infrastructures; at stake is the future of scholarship.
A new era of innovation is enabled by the integration of social sciences and information systems research. In this context, the adoption of Big Data and analytics technology brings new insight to the social sciences. It also delivers new, flexible responses to crucial social problems and challenges. We are proud to deliver this edited volume on the social impact of big data research. It is one of the first initiatives worldwide analyzing of the impact of this kind of research on individuals and social issues. The organization of the relevant debate is arranged around three pillars: Section A: Big Data Research for Social Impact: • Big Data and Their Social Impact; • (Smart) Citizens from Data Providers to Decision-Makers; • Towards Sustainable Development of Online Communities; • Sentiment from Online Social Networks; • Big Data for Innovation. Section B. Techniques and Methods for Big Data driven research for Social Sciences and Social Impact: • Opinion Mining on Social Media; • Sentiment Analysis of User Preferences; • Sustainable Urban Communities; • Gender Based Check-In Behavior by Using Social Media Big Data; • Web Data-Mining Techniques; • Semantic Network Analysis of Legacy News Media Perception. Section C. Big Data Research Strategies: • Skill Needs for Early Career Researchers--A Text Mining Approach; • Pattern Recognition through Bibliometric Analysis; • Assessing an Organization's Readiness to Adopt Big Data; • Machine Learning for Predicting Performance; • Analyzing Online Reviews Using Text Mining; • Context-Problem Network and Quantitative Method of Patent Analysis. Complementary social and technological factors including: • Big Social Networks on Sustainable Economic Development; Business Intelligence.
This volume captures the status of digital humanities within the Arts in South Africa. The primary research methodology falls within the broader tradition of phenomenological hermeneutics, with a specific emphasis on visual hermeneutics. Some of the tools utilised as part of the visual hermeneutic methods are geographic information system (GIS) mapping, sensory ethnography and narrative pathways. Digital humanities is positioned here as the necessary engagement of the humanities with the pervasive digital culture of the 21st century. It is posited that the humanities and arts, in particular, have an essential role to play in unlocking meaning from scientific, technological and data-driven research. The critical engagement with digital humanities is foregrounded throughout the volume, as this crucial engagement works through images. Images (as understood within image studies) are not merely another form of text but always more than text. As such, this book is the first of its kind in the South African scholarly landscape, and notably also a first on the African continent. Its targeted audience include both scholars within the humanities, particularly in the arts and social sciences. Researchers pursuing the new field of digital humanities may also find the ideas presented in this book significant. Several of the chapters analyse the question of dealing with digital humanities through representations of the self as viewed from the Global South. However, it should be noted that self-representation is not the only area covered in this volume. The latter chapters of the book discuss innovative ways of implementing digital humanities strategies and methodologies for teaching and researching in South Africa.
This book offers a critical introduction to the core technologies underlying the Internet from a humanistic perspective. It provides a cultural critique of computing technologies, by exploring the history of computing and examining issues related to writing, representing, archiving and searching. The book raises awareness of, and calls for, the digital humanities to address the challenges posed by the linguistic and cultural divides in computing, the clash between communication and control, and the biases inherent in networked technologies. A common problem with publications in the Digital Humanities is the dominance of the Anglo-American perspective. While seeking to take a broader view, the book attempts to show how cultural bias can become an obstacle to innovation both in the methodology and practice of the Digital Humanities. Its central point is that no technological instrument is culturally unbiased, and that all too often the geography that underlies technology coincides with the social and economic interests of its producers. The alternative proposed in the book is one of a world in which variation, contamination and decentralization are essential instruments for the production and transmission of digital knowledge. It is thus necessary not only to have spaces where DH scholars can interact (such as international conferences, THATCamps, forums and mailing lists), but also a genuine sharing of technological know-how and experience. "This is a truly exceptional work on the subject of the digital....Students and scholars new to the field of digital humanities will find in this book a gentle introduction to the field, which I cannot but think would be good and perhaps even inspirational for them....Its history of the development of machines and programs and communities bent on using computers to advance science and research merely sets the stage for an insightful analysis of the role of the digital in the way both scholars and everyday people communicate and conceive of themselves and "others" in written forms - from treatises to credit card transactions." Peter Shillingsburg The Digital Humanist is not simply a translation of the Italian book L'umanista digitale (il Mulino 2010), but a new version tailored to an international audience through the improvement and expansion of the sections on social, cultural and ethical problems of the most widely used methodologies, resources and applications. TABLE OF CONTENTS // Preface: Digital Humanities at a Political Turn? by Geoffrey Rockwell / PART I: The Socio-Historical Roots - Chap. 1: Technology and the Humanities: A History of Interaction - Chap. 2: Internet, or The Humanistic Machine / PART II: Theoretical and Practical Dimensions - Chap. 3: Writing and Content Production - Chap. 4: Representing and Archiving - Chap. 5: Searching and Organizing / Conclusions: DH in a Global Perspective
This major new manifesto offers a “clear and compelling vision of a postcapitalist society” and shows how left-wing politics can be rebuilt for the 21st century (Mark Fisher, author of Capitalist Realism) Neoliberalism isn’t working. Austerity is forcing millions into poverty and many more into precarious work, while the left remains trapped in stagnant political practices that offer no respite. Inventing the Future is a bold new manifesto for life after capitalism. Against the confused understanding of our high-tech world by both the right and the left, this book claims that the emancipatory and future-oriented possibilities of our society can be reclaimed. Instead of running from a complex future, Nick Srnicek and Alex Williams demand a postcapitalist economy capable of advancing standards, liberating humanity from work and developing technologies that expand our freedoms. This new edition includes a new chapter where they respond to their various critics.
This volume explores dynamic factor model specification, asymptotic and finite-sample behavior of parameter estimators, identification, frequentist and Bayesian estimation of the corresponding state space models, and applications.