Mapping the Demand Side of Computational Social Science for Policy
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.
Handbook of Computational Social Science for Policy
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.