Big Data and Social Science

This chapter deals with inference and the errors associated with big data. Social scientists know only too well the cost associated with bad data—we highlighted both the classic Literary Digest example and the more recent Google Flu ...

Big Data and Social Science

Both Traditional Students and Working Professionals Acquire the Skills to Analyze Social Problems. Big Data and Social Science: A Practical Guide to Methods and Tools shows how to apply data science to real-world problems in both research and the practice. The book provides practical guidance on combining methods and tools from computer science, statistics, and social science. This concrete approach is illustrated throughout using an important national problem, the quantitative study of innovation. The text draws on the expertise of prominent leaders in statistics, the social sciences, data science, and computer science to teach students how to use modern social science research principles as well as the best analytical and computational tools. It uses a real-world challenge to introduce how these tools are used to identify and capture appropriate data, apply data science models and tools to that data, and recognize and respond to data errors and limitations. For more information, including sample chapters and news, please visit the author's website.

Big Data Research for Social Sciences and Social Impact

Big. Data. Research. for. Social. Science. and. Social. Impact. Miltiadis D. Lytras1,2,* and Anna Visvizi1,3 1 ... in this Special Issue contribute to the debate on the use of big data in social sciences and big data social impact.

Big Data Research for Social Sciences and Social Impact

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.

Big Data in Computational Social Science and Humanities

With the increased number of viable cloud computing providers on the market, adopting the cloud for big data research has become a very attractive option for computational social scientists. In Chap. 15, “Cloud Computing in the Social ...

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.

Computational Social Science in the Age of Big Data

eliSaBeth günther / daMian trilling / BoB van de velde But How Do We Store It? (Big) Data Architecture in the Social-Scientific Research Process Abstract The social-scientific research process is usually considered to consist of ...

Computational Social Science in the Age of Big Data

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.

Noise Filtering for Big Data Analytics

According to Grimmer [→27], Big Data answer to fundamental questions of business, government and social sciences. In [→27], he argued to dismiss several claims which are yet to be made by computational fields ...

Noise Filtering for Big Data Analytics

This book explains how to perform data de-noising, in large scale, with a satisfactory level of accuracy. Three main issues are considered. Firstly, how to eliminate the error propagation from one stage to next stages while developing a filtered model. Secondly, how to maintain the positional importance of data whilst purifying it. Finally, preservation of memory in the data is crucial to extract smart data from noisy big data. If, after the application of any form of smoothing or filtering, the memory of the corresponding data changes heavily, then the final data may lose some important information. This may lead to wrong or erroneous conclusions. But, when anticipating any loss of information due to smoothing or filtering, one cannot avoid the process of denoising as on the other hand any kind of analysis of big data in the presence of noise can be misleading. So, the entire process demands very careful execution with efficient and smart models in order to effectively deal with it.

Social Research Methods

This chapter provides the necessary knowledge for understanding how big data and computational social science can be used to ask new questions and provide new answers in social research. • Overview of typical questions for big data ...

Social Research Methods

Structured around one of the concepts students struggle with the most—the research question—this book begins with how to understand the role of good questions before demonstrating how questions underpin good research designs and how social research can be framed as asking and answering questions. Perfect for undergraduate students new to methods, it teaches students how qualitative, quantitative, and mixed methods research can be used to answer these questions. "An incredibly resourceful book that contains a forensic insight into social research methods, offering the full range of contemporary approaches. Students will find particular value in the accessibility and detail of the text. Each chapter provides a set of learning outcomes, study questions and further reading." - Dr Ruth McAreavey, Newcastle University Supported by a website that maps online resources to key stages of the learning process, it helps students: - Understand the scientific method - Learn the vocabulary of social science research - Plan and design research - Practice with and interpret data - Explore social science literature and improve assignments with good citations - Improve critical thinking. - Extensive visualizations, overviews, examples, exercises, and other learning features, make this the perfect introductory text to build confidence and best practice around research methods.

Big Data Little Data No Data

are summarized , examining the implications for data scholarship . Methods were largely idiographic but also involved social network analysis and the design and evaluation of technologies . Together , these two case studies examine how ...

Big Data  Little Data  No Data

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.

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

for the methodologies employed in the humanities and social sciences. Second, traditional humanists should embrace the opportunities the digital humanities has to offer with a view to studying society based on large quantities of data: ...

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

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 Analytics Systems Algorithms Applications

A large diversity of topics can be studied in e-Social Science. There are many diverse electronic data sources in the Internet for e-Social Science research. These include electronic discussion networks, private, corporate or even ...

Big Data Analytics  Systems  Algorithms  Applications

This book provides a comprehensive survey of techniques, technologies and applications of Big Data and its analysis. The Big Data phenomenon is increasingly impacting all sectors of business and industry, producing an emerging new information ecosystem. On the applications front, the book offers detailed descriptions of various application areas for Big Data Analytics in the important domains of Social Semantic Web Mining, Banking and Financial Services, Capital Markets, Insurance, Advertisement, Recommendation Systems, Bio-Informatics, the IoT and Fog Computing, before delving into issues of security and privacy. With regard to machine learning techniques, the book presents all the standard algorithms for learning – including supervised, semi-supervised and unsupervised techniques such as clustering and reinforcement learning techniques to perform collective Deep Learning. Multi-layered and nonlinear learning for Big Data are also covered. In turn, the book highlights real-life case studies on successful implementations of Big Data Analytics at large IT companies such as Google, Facebook, LinkedIn and Microsoft. Multi-sectorial case studies on domain-based companies such as Deutsche Bank, the power provider Opower, Delta Airlines and a Chinese City Transportation application represent a valuable addition. Given its comprehensive coverage of Big Data Analytics, the book offers a unique resource for undergraduate and graduate students, researchers, educators and IT professionals alike.

World social science report 2016

In doing this, social scientists can establish themselves as noteworthy commentators on our realities, and thus motivate change or transformation. This is in contrast to the often narrow and specialized ways in which big data is ...

World social science report  2016

Never before has inequalit y been so high on the agenda of policy-makers worldwide, or such a hot topic for social science research. More journal ar ticles are being published on the topic of inequalit y and social justice today than ever before.

The Routledge Companion to Philosophy of Social Science

The epistemological promise of Big Data is to render social science more empirical. social science based on Big Data can be purely inductive—free from empirically vacuous social theory, prejudiced a priori selection of variables, ...

The Routledge Companion to Philosophy of Social Science

The Routledge Companion to Philosophy of Social Science is an outstanding guide to the major themes, movements, debates, and topics in the philosophy of social science. It includes thirty-seven newly written chapters, by many of the leading scholars in the field, as well as a comprehensive introduction by the editors. Insofar as possible, the material in this volume is presented in accessible language, with an eye toward undergraduate and graduate students who may be coming to some of this material for the first time. Scholars too will appreciate this clarity, along with the chance to read about the latest advances in the discipline. The Routledge Companion to Philosophy of Social Science is broken up into four parts. Historical and Philosophical Context Concepts Debates Individual Sciences Edited by two of the leading scholars in the discipline, this volume is essential reading for anyone interested in the philosophy of social science, and its many areas of connection and overlap with key debates in the philosophy of science.

Realism and Complexity in Social Science

As Savage et al noted, much “transactional data” research (which uses big data) is conducted by private companies, not social scientists and for the most part does not utilise traditional modelling techniques, but rather predictive ...

Realism and Complexity in Social Science

Realism and Complexity in Social Science is an argument for a new approach to investigating the social world, that of complex realism. Complex realism brings together a number of strands of thought, in scientific realism, complexity science, probability theory and social research methodology. It proposes that the reality of the social world is that it is probabilistic, yet there exists enough invariance to make the discovery and explanation of social objects and causal mechanisms possible. This forms the basis for the development of a complex realist foundation for social research, that utilises a number of new and novel approaches to investigation, alongside the more traditional corpus of quantitative and qualitative methods. Research examples are drawn from research in sociology, epidemiology, criminology, social policy and human geography. The book assumes no prior knowledge of realism, probability or complexity and in the early chapters, the reader is introduced to these concepts and the arguments against them. Although the book is grounded in philosophical reasoning, this is in a direct and accessible style that will appeal both to social researchers with a methodological interest and philosophers with an interest in social investigation.

Digital Methods for Social Science

social science is the new era of social media 'big data'. To put the volume of big data into perspective, public opinion polls run by the European Commission in the form of large-scale surveys involve the gathering of data from 27,000 ...

Digital Methods for Social Science

This timely book inspires researchers to deploy relevant, effective, innovative digital methods. It explores the relationship of such methods to 'mainstream' social science; interdisciplinarity; innovations in digital research tools; the opportunities (and challenges) of digital methods in researching social life; and digital research ethics.

Big Data Meets Survey Science

716 24 Moving Social Science into the Fourth Paradigm: The Data Life Cycle data, virtual lab-style experiments, and computational modelling” (Watts 2016). We can now employ computational approaches to studying social phenomena by ...

Big Data Meets Survey Science

Offers a clear view of the utility and place for survey data within the broader Big Data ecosystem This book presents a collection of snapshots from two sides of the Big Data perspective. It assembles an array of tangible tools, methods, and approaches that illustrate how Big Data sources and methods are being used in the survey and social sciences to improve official statistics and estimates for human populations. It also provides examples of how survey data are being used to evaluate and improve the quality of insights derived from Big Data. Big Data Meets Survey Science: A Collection of Innovative Methods shows how survey data and Big Data are used together for the benefit of one or more sources of data, with numerous chapters providing consistent illustrations and examples of survey data enriching the evaluation of Big Data sources. Examples of how machine learning, data mining, and other data science techniques are inserted into virtually every stage of the survey lifecycle are presented. Topics covered include: Total Error Frameworks for Found Data; Performance and Sensitivities of Home Detection on Mobile Phone Data; Assessing Community Wellbeing Using Google Street View and Satellite Imagery; Using Surveys to Build and Assess RBS Religious Flag; and more. Presents groundbreaking survey methods being utilized today in the field of Big Data Explores how machine learning methods can be applied to the design, collection, and analysis of social science data Filled with examples and illustrations that show how survey data benefits Big Data evaluation Covers methods and applications used in combining Big Data with survey statistics Examines regulations as well as ethical and privacy issues Big Data Meets Survey Science: A Collection of Innovative Methods is an excellent book for both the survey and social science communities as they learn to capitalize on this new revolution. It will also appeal to the broader data and computer science communities looking for new areas of application for emerging methods and data sources.

Contemporary Philosophy and Social Science

“Explaining the Emergence of Political Fragmentation on Social Media: The Role of Ideology and Extremism.” Journal of Computer-Mediated Communication 23 (1): 17–33. Canali, S. 2016. “Big Data, Epistemology and Causality: Knowledge In ...

Contemporary Philosophy and Social Science

How should we theorize about the social world? How can we integrate theories, models and approaches from seemingly incompatible disciplines? Does theory affect social reality? This state-of-the-art collection addresses contemporary methodological questions and interdisciplinary developments in the philosophy of social science. Facilitating a mutually enriching dialogue, chapters by leading social scientists are followed by critical evaluations from philosophers of social science. This exchange showcases recent major theoretical and methodological breakthroughs and challenges in the social sciences, as well as fruitful ways in which the analytic tools developed in philosophy of science can be applied to understand these advancements. The volume covers a diverse range of principles, methods, innovations and applications, including scientific and methodological pluralism, performativity of theories, causal inferences and applications of social science to policy and business. Taking a practice-orientated and interactive approach, it offers a new philosophy of social science grounded in and relevant to the emerging social science practice.

Evidence based Policy Making in the Social Sciences

This small case perfectly illustrates the huge advances in social science and public policy understanding that the availability of 'big data' now seems to offer. The economists could not possibly have reliably identified the subset of ...

Evidence based Policy Making in the Social Sciences

This valuable book offers a distinct and critical showcase of emerging forms of discovery for policy-making drawing on the insights of some of the world’s leading authorities in public policy analysis.

Soziologie Sociology in the German Speaking World

In general, data are considered “big” when they are digitally available and exceed the capacities of conventional analysis software owing to their large volume and complexity. Recent volumes on big data in social science research share ...

Soziologie   Sociology in the German Speaking World

This book provides the first systematic overview of German sociology today. Thirty-four chapters review current trends, relate them to international discussions and discuss perspectives for future research. The contributions span the whole range of sociological research topics, from social inequality to the sociology of body and space, addressing pressing questions in sociological theory and innovative research methods. TOC: Introduction Culture / Uta Karstein and Monika Wohlrab-Sahr Demography and Aging / François Höpflinger Economic Sociology / Andrea Maurer Education and Socialization / Matthias Grundmann Environment / Anita Engels Europe / Monika Eigmüller Family and Intimate Relationships / Dirk Konietzka, Michael Feldhaus, Michaela Kreyenfeld, and Heike Trappe (Felt) Body. Sports, Medicine, and Media / Robert Gugutzer and Claudia Peter Gender / Paula-Irene Villa and Sabine Hark Globalization and Transnationalization / Anja Weiß Global South / Eva Gerharz and Gilberto Rescher History of Sociology / Stephan Moebius Life Course / Johannes Huinink and Betina Hollstein Media and Communication / Andreas Hepp Microsociology / Rainer Schützeichel Migration / Ludger Pries Mixed-Methods and Multimethod Research / Felix Knappertsbusch, Bettina Langfeldt, and Udo Kelle Organization / Raimund Hasse Political Sociology / Jörn Lamla Qualitative Methods / Betina Hollstein and Nils C. Kumkar Quantitative Methods / Alice Barth and Jörg Blasius Religion / Matthias Koenig Science and Higher Education / Anna Kosmützky and Georg Krücken Social Inequalities―Empirical Focus / Gunnar Otte, Mara Boehle, and Katharina Kunißen Social Inequalities―Theoretical Focus / Thomas Schwinn Social Movements / Thomas Kern Social Networks / Roger Häußling Social Policy / Birgit Pfau-Effinger and Christopher Grages Social Problems / Günter Albrecht Social Theory / Wolfgang Ludwig Schneider Society / Uwe Schimank Space. Urban, Rural, Territorial / Martina Löw Technology and Innovation / Werner Rammert Work and Labor / Brigitte Aulenbacher and Johanna Grubner List of Contributors Index

Knowledge Discovery in the Social Sciences

Big data has also revolutionized scientific research with the emergence of many inter- and multidisciplinary fields. Computational social science, bioinformatics, the Sloan Digital Sky Survey in astronomy, AlphaGo in artificial ...

Knowledge Discovery in the Social Sciences

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

The Politics and Policies of Big Data

The social benefits to accrue from such social science deployments of these data logics have yet to be fully evaluated, recognised, or perhaps appreciated – but nevertheless remain open to such questions. However the case of 'Big' ...

The Politics and Policies of Big Data

Big Data, gathered together and re-analysed, can be used to form endless variations of our persons - so-called ‘data doubles’. Whilst never a precise portrayal of who we are, they unarguably contain glimpses of details about us that, when deployed into various routines (such as management, policing and advertising) can affect us in many ways. How are we to deal with Big Data? When is it beneficial to us? When is it harmful? How might we regulate it? Offering careful and critical analyses, this timely volume aims to broaden well-informed, unprejudiced discourse, focusing on: the tenets of Big Data, the politics of governance and regulation; and Big Data practices, performance and resistance. An interdisciplinary volume, The Politics of Big Data will appeal to undergraduate and postgraduate students, as well as postdoctoral and senior researchers interested in fields such as Technology, Politics and Surveillance.

Interdiscipline

Social sciences Compare what DH does with the much-maligned (in literary studies) social scientific use of big data, and the version of DH described in PMLA special issue gets stranger. My primary source is a work published in 2019, ...

Interdiscipline

This book brings together two different discussions on the value of the humanities and a broader debate on interdisciplinary scholarship in order to propose a new way beyond current threats to the humanities. Petar Ramadanovic offers nothing short of a drastic rehaul of our approaches to literary scholarship, the humanities, and university systems. Beginning with an analysis of what is often referred to as the "crises" in the humanities, the author looks at the specifics of literary studies, but also issues around working conditions for academics. From precarity and pay conditions to peer review, the book has practical as well as theoretical implications that will resonate throughout the humanities. While most books defending the humanities emphasize the uniqueness of the subject or area, Ramadanovic does the opposite, emphasizing the need for interdisciplinarity and combined knowledge. This proposal is then fully explored through literary studies, and its potential throughout the humanities and beyond, into the sciences. Interdiscipline is not just a defense of literature and the humanities; it offers a clear and inspiring pathway forwards, drawing on all disciplines to show their cultural and social significance. The book is important reading for all scholars of literary studies, and also throughout the humanities.