This book contains a selection of the latest research in the field of Computational Social Science (CSS) methods, uses, and results, as presented at the 2021 annual conference of the Computational Social Science Society of the Americas (CSSSA). Computational social science (CSS) is the science that investigates social and behavioral dynamics through social simulation, social network analysis, and social media analysis. The CSSSA is a professional society that aims to advance the field of computational social science in all areas, including basic and applied orientations, by holding conferences and workshops, promoting standards of scientific excellence in research and teaching, and publishing research findings and results.
This book contains a selection of the latest research in the field of computational social science (CSS) methods, uses, and results, as presented at the 2022 annual conference of the Computational Social Science Society of the Americas (CSSSA). Computational social science (CSS) is the science that investigates social and behavioral dynamics through social simulation, social network analysis, and social media analysis. The CSSSA is a professional society that aims to advance the field of computational social science in all areas, including basic and applied orientations, by holding conferences and workshops, promoting standards of scientific excellence in research and teaching, and publishing research findings and results.
This book is comprised of the latest research into CSS methods, uses, and results, as presented at the 2020 annual conference of the Computational Social Science Society of the Americas (CSSSA). Computational social science (CSS) is the science that investigates social and behavioral dynamics through social simulation, social network analysis, and social media analysis. The CSSSA is a professional society that aims to advance the field of computational social science in all areas, including basic and applied orientations, by holding conferences and workshops, promoting standards of scientific excellence in research and teaching, and publishing research findings and results. The above-mentioned conference was held virtually, October 8 – 11, 2020. What follows is a diverse representation of new results and approaches to using the tools of CSS and agent-based modeling (ABM) in exploring complex phenomena across many different domains. Readers will therefore not only have the results of these specific projects upon which to build, along with a wealth of case-study examples that can serve as meaningful exemplars for new research projects and activities, they will also gain a greater appreciation for the broad scope of CSS.
This book presents the latest research into CSS methods, uses, and results, as presented at the 2019 annual conference of the CSSSA. This conference was held in Santa Fe, New Mexico, October 24 – 27, 2019, at the Drury Plaza Hotel. What follows is a diverse representation of new results and approaches for using the tools of CSS and agent-based modeling (ABM) for exploring complex phenomena across many different domains. Readers will therefore not only have the results of these specific projects on which to build, but will also gain a greater appreciation for the broad scope of CSS, and have a wealth of case-study examples that can serve as meaningful exemplars for new research projects and activities. The Computational Social Science Society of the Americas (CSSSA) is a professional society that aims to advance the field of CSS in all its areas, from fundamental principles to real-world applications, by holding conferences and workshops, promoting standards of scientific excellence in research and teaching, and publishing novel research findings.
The book consists of 8 parts: Energy Informatics, Electric Power Engineering, Heat Power Engineering, Nuclear Power Engineering, Renewable Power Engineering, Fuels, Transport, and Environmental Safety. The results presented in this book are aimed at solving some of the technical issues proposed by the Ukraine Recovery Plan and other important scientific and applied problems in the field of energy. Scientists from leading Ukrainian academic institutions and universities are working on this book. This book is for scientists, researchers, engineers, as well as lecturers and postgraduates of higher education institutions dealing with energy sector, power systems, ecological safety, etc.
This book uniquely combines the authors’ personal experiences, deep cultural and professional experience of living and working in Russia and the former USSR, and interest and experience with language and computational analysis, to shed light on a highly contemporary question: what is motivating conflict and unrest in Russia and its surrounding countries? How does Russian government suppression of information manifest in practice today, and how does it fit into the historical cultural pattern for Russia? The authors take a computational look at social and traditional media in the original languages, from Russia, Ukraine, Lithuania, Latvia, Estonia, Poland, and the English-speaking world, to glean insights and separate fact from fiction. This book helps readers interested in Eastern Europe to ‘take the temperature’ of the region today, but it is also of interest to readers in the policy and analysis community, because it offers a template, an analytical ‘how-to’ guide which aims to follow in the footsteps of CIA author Richards Heuer’s ‘Psychology of Intelligence Analysis’, to show how state-of-the-art computational analysis techniques could be applied to similar problems in other topic areas, with the human analyst and computational techniques each working together to create a whole greater than the sum of its parts.
This book captures some of the emergent topics and methods in demography at the turn of this 21st century. Like all social sciences, the concerns and tools of demography must evolve with the times. As new technologies expand data management opportunities, and as a changing world faces new demographic issues, the field of demographic research must expand as well. The chapters in the book rise to this challenge by embracing new questions or new approaches to classic questions about demographic processes and their link to development, including inequality, health, migration, and youth across the world.
Data stealing is a major concern on the internet as hackers and criminals have begun using simple tricks to hack social networks and violate privacy. Cyber-attack methods are progressively modern, and obstructing the attack is increasingly troublesome, regardless of whether countermeasures are taken. The Dark Web especially presents challenges to information privacy and security due to anonymous behaviors and the unavailability of data. To better understand and prevent cyberattacks, it is vital to have a forecast of cyberattacks, proper safety measures, and viable use of cyber-intelligence that empowers these activities. Dark Web Pattern Recognition and Crime Analysis Using Machine Intelligence discusses cyberattacks, security, and safety measures to protect data and presents the shortcomings faced by researchers and practitioners due to the unavailability of information about the Dark Web. Attacker techniques in these Dark Web environments are highlighted, along with intrusion detection practices and crawling of hidden content. Covering a range of topics such as malware and fog computing, this reference work is ideal for researchers, academicians, practitioners, industry professionals, computer scientists, scholars, instructors, and students.
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.
