Abstract: This report aims at collecting novel and pressing policy issues that can be addressed by Computational Social Science (CSS), an emerging discipline that is rooted in the increasing availability of digital trace data and computational resources and seeks to apply data science methods to social sciences. The questions were sourced from researchers at the European Commission who work at the interface between science and policy and who are well positioned to formulate research questions that are likely to anticipate future policy needs.
The attempt is to identify possible directions for Computational Social Science starting from the demand side, making it an effort to consider not only how science can ultimately provide policy support — “Science for Policy – but also how policymakers can be involved in the process of defining and co-creating the CSS4P agenda from the outset — ‘Policy for Science’. The report is expected to raise awareness on the latest scientific advances in Computational Social Science and on its potential for policy, integrating the knowledge of policymakers and stimulating further questions in the context of future developments of this initiative.
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.
The phenomenon of Business-to-Government (B2G) data sharing represents a growing trend, especially in latest years. In fact, research has shown how privately held data could have a huge potential when used to tackle societal policy issues. B2G data sharing initiatives can be employed in different situations: from emergencies to the construction of official statistics and the use in research, just to name a few. In all these circumstances, the quality level required for the data may be different, as different principles could prevail upon others (e.g., timeliness in the case of emergencies is a key parameter). This heterogeneity in possible use-cases motivates the present work. In fact, our objective is to understand and classify the different contexts in which B2G data sharing may happen. The idea is to create a taxonomy of B2G data sharing initiatives, in which we identify all the different instances where B2G data sharing may occur. Afterwards we add as attributes some identified quality principles that characterise the different B2G data sharing situations. The work aims at providing further information that can help clarify specificities and requirements of B2G data sharing in order to enable relevant data flows and make them more dynamic.
This work, carried out in collaboration with Unit B.3 Territorial Development of the Joint Research Centre, is composed by two studies:
- a quantitative analysis, that aims at studying the possible effect of Airbnb on real estate prices
- a qualitative study, that aims at studying the impact of tourism on the livability of cities.
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Concerning data protection aspects of the qualitative study, the privacy statement is available here: