We are living in a digital era in which most of our daily activities take place online. This has created a big data phenomenon that has been subject to scientific research with increasingly available tools and processing power. As a result, a growing number of social science scholars are using computational methods for analyzing social behavior. To further the area, these evolving methods must be made known to sociological research scholars. Opportunities and Challenges for Computational Social Science Methods focuses on the implementation of social science methods and the opportunities and challenges of these methods. This book sheds light on the infrastructure that should be built to gain required skillsets, the tools used in computational social sciences, and the methods developed and applied into computational social sciences. Covering topics like computational communication, ecological cognition, and natural language processing, this book is an essential resource for researchers, data scientists, scholars, students, professors, sociologists, and academicians.

The interest of physicists in economic and social questions is not new: for over four decades, we have witnessed the emergence of what is called nowadays “sociophysics” and “econophysics”, vigorous and challenging areas within the wider “Interdisciplinary Physics”. With tools borrowed from Statistical Physics and Complexity, this new area of study have already made important contributions, which in turn have fostered the development of novel theoretical foundations in Social Science and Economics, via mathematical approaches, agent-based modelling and numerical simulations. From these foundations, Computational Social Science has grown to incorporate as well the empirical component --aided by the recent data deluge from the Web 2.0 and 3.0--, closing in this way the experiment-theory cycle in the best tradition of Physics.

Providing an up-to-date portrait of the concepts and methods of analytical sociology, this pivotal Research Handbook traces the historical evolution of the field, utilising key research examples to illustrate its core principles. It investigates how analytical sociology engages with other approaches such as analytical philosophy, structural individualism, social stratification research, complexity science, pragmatism, and critical realism, exploring the foundations of the topic as well as its major explanatory mechanisms and methods.

Your #1 resource for carrying out educational research as part of postgraduate study. High-quality educational research requires careful consideration of every aspect of the process. This all-encompassing textbook written by leading international experts gives you considered overview of principles that underpin research, and key qualitative, quantitative and mixed methods for research design, data collection and analysis. This third edition includes four new chapters: Disseminating your research Data science and computational research methods Observational methods Analysis of variance (ANOVA) Plus a new Research essentials feature that highlights key ‘must-haves’ or misconceptions relating to each methodological approach, research design or analytical tool discussed. This is essential reading for postgraduate students on education courses and early career researchers looking to sharpen their research practice.

The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This first volume focuses on the scope of computational social science, ethics, and case studies. It covers a range of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introduces digital trace data, reviews the changing survey landscape, and presents novel examples of computational social science research on sensing social interaction, social robots, bots, sentiment, manipulation, and extremism in social media. The volume not only makes major contributions to the consolidation of this growing research field but also encourages growth in new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates, and researchers engaging with computational methods across the social sciences, as well as those within the scientifi c and engineering sectors.

This two-volume book constitutes the post-conference proceedings of the 5th International Conference on Advances in Computing and Data Sciences, ICACDS 2021, held in Nashik, India, in April 2021.* The 103 full papers were carefully reviewed and selected from 781 submissions. Part II is devoted to data sciences, organizing principles, medical technologies, computational linguistics etc. *The conference was held virtually due to the COVID-19 pandemic.

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.

This open access handbook describes foundational issues, methodological approaches and examples on how to analyse and model data using Computational Social Science (CSS) for policy support. Up to now, CSS studies have mostly developed on a small, proof-of concept, scale that prevented from unleashing its potential to provide systematic impact to the policy cycle, as well as from improving the understanding of societal problems to the definition, assessment, evaluation, and monitoring of policies. The aim of this handbook is to fill this gap by exploring ways to analyse and model data for policy support, and to advocate the adoption of CSS solutions for policy by raising awareness of existing implementations of CSS in policy-relevant fields. To this end, the book explores applications of computational methods and approaches like big data, machine learning, statistical learning, sentiment analysis, text mining, systems modelling, and network analysis to different problems in the social sciences. The book is structured into three Parts: the first chapters on foundational issues open with an exposition and description of key policymaking areas where CSS can provide insights and information. In detail, the chapters cover public policy, governance, data justice and other ethical issues. Part two consists of chapters on methodological aspects dealing with issues such as the modelling of complexity, natural language processing, validity and lack of data, and innovation in official statistics. Finally, Part three describes the application of computational methods, challenges and opportunities in various social science areas, including economics, sociology, demography, migration, climate change, epidemiology, geography, and disaster management. The target audience of the book spans from the scientific community engaged in CSS research to policymakers interested in evidence-informed policy interventions, but also includes private companies holding data that can be used to study social sciences and are interested in achieving a policy impact.

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.