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Full Description
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 holdingdata that can be used to study social sciences and are interested in achieving a policy impact.
Contents
- 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.
