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  • Book
  • Open Access
  • © 2023

Handbook of Computational Social Science for Policy

  • 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

Buying options

Softcover Book USD 49.99
Price excludes VAT (USA)
Hardcover Book USD 59.99
Price excludes VAT (USA)

Table of contents (24 chapters)

  1. Front Matter

    Pages i-xxi
  2. Foundational Issues

    1. Front Matter

      Pages 1-1
    2. Computational Social Science for Public Policy

      • Helen Margetts, Cosmina Dorobantu
      Pages 3-18Open Access
    3. Data Justice, Computational Social Science and Policy

      • Linnet Taylor
      Pages 41-56Open Access
    4. The Ethics of Computational Social Science

      • David Leslie
      Pages 57-104Open Access
  3. Methodological Aspects

    1. Front Matter

      Pages 105-105
    2. From Lack of Data to Data Unlocking

      • Nuno Crato
      Pages 125-139Open Access
    3. Natural Language Processing for Policymaking

      • Zhijing Jin, Rada Mihalcea
      Pages 141-162Open Access
    4. Describing Human Behaviour Through Computational Social Science

      • Giuseppe A. Veltri
      Pages 163-176Open Access
    5. Challenges and Opportunities of Computational Social Science for Official Statistics

      • Serena Signorelli, Matteo Fontana, Lorenzo Gabrielli, Michele Vespe
      Pages 195-211Open Access
  4. Applications

    1. Front Matter

      Pages 213-213
    2. Agriculture, Food and Nutrition Security: Concept, Datasets and Opportunities for Computational Social Science Applications

      • T. S. Amjath-Babu, Santiago Lopez Riadura, Timothy J. Krupnik
      Pages 215-229Open Access
    3. Changing Job Skills in a Changing World

      • Joanna Napierala, Vladimir Kvetan
      Pages 243-259Open Access
    4. Digital Epidemiology

      • Yelena Mejova
      Pages 279-303Open Access
    5. Learning Analytics in Education for the Twenty-First Century

      • Kristof De Witte, Marc-André Chénier
      Pages 305-326Open Access

About this book

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.



  • Open Access
  • Computational Social Science
  • Data Science
  • Big Data Analytics
  • Statistical Learning
  • Machine Learning
  • Sentiment Analysis
  • Natural Language Processing

Editors and Affiliations

  • Scientific Development Unit, Centre for Advanced Studies, Science and Art European Commission - Joint Research Centre, Ispra, Italy

    Eleonora Bertoni, Matteo Fontana, Lorenzo Gabrielli, Serena Signorelli

  • Digital Economy Unit, European Commission - Joint Research Centre, Ispra, Italy

    Michele Vespe

About the editors

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 have been 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.

Bibliographic Information

  • Book Title: Handbook of Computational Social Science for Policy

  • Editors: Eleonora Bertoni, Matteo Fontana, Lorenzo Gabrielli, Serena Signorelli, Michele Vespe

  • DOI:

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Rightsholder (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023

  • License: CC BY

  • Hardcover ISBN: 978-3-031-16623-5Published: 25 January 2023

  • Softcover ISBN: 978-3-031-16626-6Published: 25 January 2023

  • eBook ISBN: 978-3-031-16624-2Published: 23 January 2023

  • Edition Number: 1

  • Number of Pages: XXI, 490

  • Number of Illustrations: 1 b/w illustrations

  • Topics: Data Science, Data Analysis and Big Data, Sociological Methods, Machine Learning

Buying options

Softcover Book USD 49.99
Price excludes VAT (USA)
Hardcover Book USD 59.99
Price excludes VAT (USA)