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Reinventing the Social Scientist and Humanist in the Era of Big Data

Reinventing the Social Scientist and Humanist in the Era of Big Data
Author: Susan Brokensha
Publisher: UJ Press
Total Pages: 205
Release: 2019-12-01
Genre: Social Science
ISBN: 1928424376

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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.


Reinventing the Social Scientist and Humanist in the Era of Big Data: A Perspective from South African Scholars

Reinventing the Social Scientist and Humanist in the Era of Big Data: A Perspective from South African Scholars
Author: Susan Brokensha
Publisher: UJ Press
Total Pages: 208
Release: 2019-12-01
Genre: Computers
ISBN:

Download Reinventing the Social Scientist and Humanist in the Era of Big Data: A Perspective from South African Scholars Book in PDF, ePub and Kindle

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.


Big Data and Social Science

Big Data and Social Science
Author: Ian Foster
Publisher: CRC Press
Total Pages: 413
Release: 2020-11-17
Genre: Mathematics
ISBN: 1000208591

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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.


Big Data in Computational Social Science and Humanities

Big Data in Computational Social Science and Humanities
Author: Shu-Heng Chen
Publisher: Springer
Total Pages: 388
Release: 2018-11-21
Genre: Computers
ISBN: 3319954652

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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.


Big Data Research for Social Sciences and Social Impact

Big Data Research for Social Sciences and Social Impact
Author: Miltiadis D. Lytras
Publisher: MDPI
Total Pages: 416
Release: 2020-03-19
Genre: Technology & Engineering
ISBN: 3039282204

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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.


Data Mining for the Social Sciences

Data Mining for the Social Sciences
Author: Paul Attewell
Publisher: Univ of California Press
Total Pages: 264
Release: 2015-05
Genre: Computers
ISBN: 0520280989

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"The amount of information collected on human behavior every day is staggering, and exponentially greater than at any time in the past. At the same time, we are inundated by stories of powerful algorithms capable of churning through this sea of data and uncovering patterns. These techniques go by many names - data mining, predictive analytics, machine learning - and they are being used by governments as they spy on citizens and by huge corporations are they fine-tune their advertising strategies. And yet social scientists continue mainly to employ a set of analytical tools developed in an earlier era when data was sparse and difficult to come by. In this timely book, Paul Attewell and David Monaghan provide a simple and accessible introduction to Data Mining geared towards social scientists. They discuss how the data mining approach differs substantially, and in some ways radically, from that of conventional statistical modeling familiar to most social scientists. They demystify data mining, describing the diverse set of techniques that the term covers and discussing the strengths and weaknesses of the various approaches. Finally they give practical demonstrations of how to carry out analyses using data mining tools in a number of statistical software packages. It is the hope of the authors that this book will empower social scientists to consider incorporating data mining methodologies in their analytical toolkits"--Provided by publisher.


Knowledge Discovery in the Social Sciences

Knowledge Discovery in the Social Sciences
Author: Xiaoling Shu
Publisher: University of California Press
Total Pages: 263
Release: 2020-02-04
Genre: Social Science
ISBN: 0520339991

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Knowledge Discovery in the Social Sciences helps readers find valid, meaningful, and useful information. It is written for researchers and data analysts as well as students who have no prior experience in statistics or computer science. Suitable for a variety of classes—including upper-division courses for undergraduates, introductory courses for graduate students, and courses in data management and advanced statistical methods—the book guides readers in the application of data mining techniques and illustrates the significance of newly discovered knowledge. Readers will learn to: • appreciate the role of data mining in scientific research • develop an understanding of fundamental concepts of data mining and knowledge discovery • use software to carry out data mining tasks • select and assess appropriate models to ensure findings are valid and meaningful • develop basic skills in data preparation, data mining, model selection, and validation • apply concepts with end-of-chapter exercises and review summaries


Computational Social Science in the Age of Big Data

Computational Social Science in the Age of Big Data
Author: Martin Welker
Publisher: Herbert von Halem Verlag
Total Pages: 462
Release: 2018-02-19
Genre: Business & Economics
ISBN: 3869622687

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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.


Big Data, Little Data, No Data

Big Data, Little Data, No Data
Author: Christine L. Borgman
Publisher: MIT Press
Total Pages: 411
Release: 2017-02-03
Genre: Language Arts & Disciplines
ISBN: 0262529912

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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.