Big Data In Computational Social Science And Humanities

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.

The field of Soft Computing in Humanities and Social Sciences is at a turning point. The strong distinction between “science” and “humanities” has been criticized from many fronts and, at the same time, an increasing cooperation between the so-called “hard sciences” and “soft sciences” is taking place in a wide range of scientific projects dealing with very complex and interdisciplinary topics. In the last fifteen years the area of Soft Computing has also experienced a gradual rapprochement to disciplines in the Humanities and Social Sciences, and also in the field of Medicine, Biology and even the Arts, a phenomenon that did not occur much in the previous years. The collection of this book presents a generous sampling of the new and burgeoning field of Soft Computing in Humanities and Social Sciences, bringing together a wide array of authors and subject matters from different disciplines. Some of the contributors of the book belong to the scientific and technical areas of Soft Computing while others come from various fields in the humanities and social sciences such as Philosophy, History, Sociology or Economics. Rudolf Seising received a Ph.D. degree in philosophy of science and a postdoctoral lecture qualification (PD) in history of science from the Ludwig Maximilians University of Munich. He is an Adjoint Researcher at the European Centre for Soft Computing in Mieres (Asturias), Spain. Veronica Sanz earned a Ph.D. in Philosophy at the University Complutense of Madrid (Spain). At the moment she is a Postdoctoral Researcher at the Science, Technology and Society Center in the University of California at Berkeley. Veronica Sanz earned a Ph.D. in Philosophy at the University Complutense of Madrid (Spain). At the moment she is a Postdoctoral Researcher at the Science, Technology and Society Center in the University of California at Berkeley.

Der Sammelband Computational Social Science in the Age of Big Data beschäftigt sich mit Konzepten, Methoden, Tools und Anwendungen (automatisierter) datengetriebener Forschung mit sozialwissenschaftlichem Hintergrund. Der Fokus des Bandes liegt auf der Etablierung der Computational Social Science (CSS) als aufkommendes Forschungs- und Anwendungsfeld. Es werden Beiträge international namhafter Autoren präsentiert, die forschungs- und praxisrelevante Themen dieses Bereiches besprechen. Die Herausgeber forcieren dabei einen interdisziplinären Zugang zum Feld, der sowohl Online-Forschern aus der Wissenschaft wie auch aus der angewandten Marktforschung einen Einstieg bietet.

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.

Quantitative research in social science research is changing rapidly. Researchers have vast and complex arrays of data with which to work: we have incredible tools to sift through the data and recognize patterns in that data; there are now many sophisticated models that we can use to make sense of those patterns; and we have extremely powerful computational systems that help us accomplish these tasks quickly. This book focuses on some of the extraordinary work being conducted in computational social science - in academia, government, and the private sector - while highlighting current trends, challenges, and new directions. Thus, Computational Social Science showcases the innovative methodological tools being developed and applied by leading researchers in this new field. The book shows how academics and the private sector are using many of these tools to solve problems in social science and public policy.

This book describes how powerful computing technology, emerging big and open data sources, and theoretical perspectives on spatial synthesis have revolutionized the way in which we investigate social sciences and humanities. It summarizes the principles and applications of human-centered computing and spatial social science and humanities research, thereby providing fundamental information that will help shape future research. The book illustrates how big spatiotemporal socioeconomic data facilitate the modelling of individuals’ economic behavior in space and time and how the outcomes of such models can reveal information about economic trends across spatial scales. It describes how spatial social science and humanities research has shifted from a data-scarce to a data-rich environment. The chapters also describe how a powerful analytical framework for identifying space-time research gaps and frontiers is fundamental to comparative study of spatiotemporal phenomena, and how research topics have evolved from structure and function to dynamic and predictive. As such this book provides an interesting read for researchers, students and all those interested in computational and spatial social sciences and humanities.

This book constitutes the refereed post-proceedings of the Second IFIP WG 12.7 International Workshop on Computational History and Data-Driven Humanities, held in Dublin, Ireland, in May 2016. The 7 full papers presented together with 2 invited talks and 4 lightning talks were carefully reviewed and selected from 14 submissions. The papers focus on the challenge and opportunities of data-driven humanities and cover topics at the interface between computer science, social science, humanities, and mathematics.

Big Data and Social Science: Data Science Methods and Tools for Research and Practice, Second Edition shows how to apply data science to real-world problems, covering all stages of a data-intensive social science or policy project. Prominent leaders in the social sciences, statistics, and computer science as well as the field of data science provide a unique perspective on how to apply modern social science research principles and current analytical and computational tools. The text teaches you how to identify and collect appropriate data, apply data science methods and tools to the data, and recognize and respond to data errors, biases, and limitations. Features: Takes an accessible, hands-on approach to handling new types of data in the social sciences Presents the key data science tools in a non-intimidating way to both social and data scientists while keeping the focus on research questions and purposes Illustrates social science and data science principles through real-world problems Links computer science concepts to practical social science research Promotes good scientific practice Provides freely available workbooks with data, code, and practical programming exercises, through Binder and GitHub New to the Second Edition: Increased use of examples from different areas of social sciences New chapter on dealing with Bias and Fairness in Machine Learning models Expanded chapters focusing on Machine Learning and Text Analysis Revamped hands-on Jupyter notebooks to reinforce concepts covered in each chapter This classroom-tested book fills a major gap in graduate- and professional-level data science and social science education. It can be used to train a new generation of social data scientists to tackle real-world problems and improve the skills and competencies of applied social scientists and public policy practitioners. It empowers you to use the massive and rapidly growing amounts of available data to interpret economic and social activities in a scientific and rigorous manner.

Pairing full-length scholarly essays with shorter pieces drawn from scholarly blogs and conference presentations, as well as commissioned interviews and position statements, Debates in the Digital Humanities 2016 reveals a dynamic view of a field in negotiation with its identity, methods, and reach. Pieces in the book explore how DH can and must change in response to social justice movements and events like #Ferguson; how DH alters and is altered by community college classrooms; and how scholars applying DH approaches to feminist studies, queer studies, and black studies might reframe the commitments of DH analysts. Numerous contributors examine the movement of interdisciplinary DH work into areas such as history, art history, and archaeology, and a special forum on large-scale text mining brings together position statements on a fast-growing area of DH research. In the multivalent aspects of its arguments, progressing across a range of platforms and environments, Debates in the Digital Humanities 2016 offers a vision of DH as an expanded field—new possibilities, differently structured. Published simultaneously in print, e-book, and interactive webtext formats, each DH annual will be a book-length publication highlighting the particular debates that have shaped the discipline in a given year. By identifying key issues as they unfold, and by providing a hybrid model of open-access publication, these volumes and the Debates in the Digital Humanities series will articulate the present contours of the field and help forge its future. Contributors: Moya Bailey, Northeastern U; Fiona Barnett; Matthew Battles, Harvard U; Jeffrey M. Binder; Zach Blas, U of London; Cameron Blevins, Rutgers U; Sheila A. Brennan, George Mason U; Timothy Burke, Swarthmore College; Rachel Sagner Buurma, Swarthmore College; Micha Cárdenas, U of Washington–Bothell; Wendy Hui Kyong Chun, Brown U; Tanya E. Clement, U of Texas–Austin; Anne Cong-Huyen, Whittier College; Ryan Cordell, Northeastern U; Tressie McMillan Cottom, Virginia Commonwealth U; Amy E. Earhart, Texas A&M U; Domenico Fiormonte, U of Roma Tre; Paul Fyfe, North Carolina State U; Jacob Gaboury, Stony Brook U; Kim Gallon, Purdue U; Alex Gil, Columbia U; Brian Greenspan, Carleton U; Richard Grusin, U of Wisconsin, Milwaukee; Michael Hancher, U of Minnesota; Molly O’Hagan Hardy; David L. Hoover, New York U; Wendy F. Hsu; Patrick Jagoda, U of Chicago; Jessica Marie Johnson, Michigan State U; Steven E. Jones, Loyola U; Margaret Linley, Simon Fraser U; Alan Liu, U of California, Santa Barbara; Elizabeth Losh, U of California, San Diego; Alexis Lothian, U of Maryland; Michael Maizels, Wellesley College; Mark C. Marino, U of Southern California; Anne B. McGrail, Lane Community College; Bethany Nowviskie, U of Virginia; Julianne Nyhan, U College London; Amanda Phillips, U of California, Davis; Miriam Posner, U of California, Los Angeles; Rita Raley, U of California, Santa Barbara; Stephen Ramsay, U of Nebraska–Lincoln; Margaret Rhee, U of Oregon; Lisa Marie Rhody, Graduate Center, CUNY; Roopika Risam, Salem State U; Stephen Robertson, George Mason U; Mark Sample, Davidson College; Jentery Sayers, U of Victoria; Benjamin M. Schmidt, Northeastern U; Scott Selisker, U of Arizona; Jonathan Senchyne, U of Wisconsin, Madison; Andrew Stauffer, U of Virginia; Joanna Swafford, SUNY New Paltz; Toniesha L. Taylor, Prairie View A&M U; Dennis Tenen; Melissa Terras, U College London; Anna Tione; Ted Underwood, U of Illinois, Urbana–Champaign; Ethan Watrall, Michigan State U; Jacqueline Wernimont, Arizona State U; Laura Wexler, Yale U; Hong-An Wu, U of Illinois, Urbana–Champaign.

Best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of large datasets. Human-centered data science is a new interdisciplinary field that draws from human-computer interaction, social science, statistics, and computational techniques. This book, written by founders of the field, introduces best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of very large datasets. It offers a brief and accessible overview of many common statistical and algorithmic data science techniques, explains human-centered approaches to data science problems, and presents practical guidelines and real-world case studies to help readers apply these methods. The authors explain how data scientists’ choices are involved at every stage of the data science workflow—and show how a human-centered approach can enhance each one, by making the process more transparent, asking questions, and considering the social context of the data. They describe how tools from social science might be incorporated into data science practices, discuss different types of collaboration, and consider data storytelling through visualization. The book shows that data science practitioners can build rigorous and ethical algorithms and design projects that use cutting-edge computational tools and address social concerns.