Handbook of Computational Social Science for Policy
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.
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Persons
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.