Handbook of Computational Social Science for Policy
Editat de Eleonora Bertoni, Matteo Fontana, Lorenzo Gabrielli, Serena Signorelli, Michele Vespeen Limba Engleză Paperback – 25 ian 2023
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 holdingdata that can be used to study social sciences and are interested in achieving a policy impact.
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Specificații
ISBN-13: 9783031166266
ISBN-10: 3031166264
Pagini: 490
Ilustrații: XXI, 490 p. 1 illus.
Dimensiuni: 155 x 235 x 30 mm
Greutate: 0.71 kg
Ediția:1st ed. 2023
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
ISBN-10: 3031166264
Pagini: 490
Ilustrații: XXI, 490 p. 1 illus.
Dimensiuni: 155 x 235 x 30 mm
Greutate: 0.71 kg
Ediția:1st ed. 2023
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
Cuprins
- Part I Foundational Issues. - 1. Computational Social Science for Public Policy. - 2. Computational Social Science for the Public Good: Towards a Taxonomy of Governance and Policy Challenges. - 3. Data Justice, Computational Social Science and Policy. - 4. The Ethics of Computational Social Science. - Part II Methodological Aspects. - 5. Modelling Complexity with Unconventional Data: Foundational Issues in Computational Social Science. - 6. From Lack of Data to Data Unlocking. - 7. Natural Language Processing for Policymaking. - 8. Describing Human Behaviour Through Computational Social Science. - 9. Data and Modelling for the Territorial Impact Assessment (TIA) of Policies. - 10. Challenges and Opportunities of Computational Social Science for Official Statistics. - Part III Applications. - 11. Agriculture, Food and Nutrition Security: Concept, Datasets and Opportunities for Computational Social Science Applications. - 12. Big Data and Computational Social Science for Economic Analysis and Policy. - 13. Changing Job Skills in a Changing World. - 14. Computational Climate Change: How Data Science and Numerical Models Can Help Build Good Climate Policies and Practices. - 15. Digital Epidemiology. - 16. Learning Analytics in Education for the Twenty-First Century. - 17. Leveraging Digital and Computational Demography for Policy Insights. - 18. New Migration Data: Challenges and Opportunities. - 19. New Data and Computational Methods Opportunities to Enhance the Knowledge Base of Tourism. - 20. Computational Social Science for Policy and Quality of Democracy: Public Opinion, Hate Speech, Misinformation, and Foreign Influence Campaigns. - 21. Social Interactions, Resilience, and Access to Economic Opportunity: A Research Agenda for the Field of Computational Social Science. - 22. Social Media Contribution to the Crisis Management Processes: Towards a More Accurate Response Integrating Citizen-Generated Content and Citizen-Led Activities. - 23. The Empirical Study of Human Mobility: Potentials and Pitfalls of Using Traditional and Digital Data. - 24. Towards a More Sustainable Mobility.
Notă biografică
Eleonora Bertoni is a Project Officer – Computational Social Scientist at the European Commission, Joint Research Centre (JRC), where she works for the Centre of Advanced Studies (CAS). She coordinates the activities of the CAS group on Computational Social Science for Policy which aims at building capacity in accessing and analysing non-traditional data, as well as exploring applications of computational methods in different social sciences domains to address specific policy questions.
Matteo Fontana is a Project Officer - Data Scientist at the Joint Research Centre of the European Commission. His main research interest is the application and development of data science and statistical learning techniques to evaluate complex data sources in the social sciences field. He is particularly interested in nonparametric inference and prediction, with a focus on conformal methods for complex data. From an applicative point of view, he is interested in macro-economic forecasting, migration modelling and environmental economics.
Lorenzo Gabrielli is a Data Scientist in the JRC Centre for Advanced Studies (CAS) Project on Computational Social Science for Policy to carry out scientific tasks, i.e. harness non-traditional data including big data, analyse it and draw conclusions on its impact on society. He has gained experience in the analysis of big data with data mining and machine learning techniques in national and international contexts by collaborating with several public and private research institutes.
Serena Signorelli works as a Data Partnership and Management Officer at the Joint Research Centre. Her current project focuses on Computational Social Science for Policy, and it is part of the Centre for Advanced Studies of the Scientific Development unit. Her research interests have mainly focused on the use of Wikipedia page views to study tourism flows, and they havebeen exploited through a traineeship and a subsequent contract with the Eurostat Big Data task force.
Michele Vespe is a Team Leader at the European Commission, Joint Research Centre, where he coordinates the activities of teams of researchers for investigating societal consequences associated with the improved availability of digital trace data, including research in the fields of data governance. He also leads the Computational Social Science for Policy project team.
Textul de pe ultima copertă
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 problemsin 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 datathat can be used to study social sciences and are interested in achieving a policy impact.
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 problemsin 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 datathat can be used to study social sciences and are interested in achieving a policy impact.
Caracteristici
This book is open access, which means that you have free and unlimited access Proposes how to analyse and model data for policy support by exploiting CSS methods for policy Applies CSS methods in various areas including economics, education, migration, climate change, or disaster management Applies to CSS researchers as well as policymakers interested in evidence-based policy interventions