HORIZON EUROPE┋Computational Social Science approaches in research on democracy


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Expected Outcome

Projects should contribute to all of the following expected outcomes:

  • Insights into various aspects of democracy, its institutions, its capacity to adapt to changing circumstances, interaction with structural socio-economic patterns utilising Computational Social Science[1] (CSS) to identify systematic patterns to test working hypotheses.
  • Develop and test methodologies that combine and integrate CSS and other Social Sciences and Humanities (SSH) methods to study democratic governance, overcoming traditional academic boundaries in the field and producing synthetic data and simulation environments to stage full scale experiments which otherwise are reserved to historical study.
  • Use of critical approaches to data and datafication of social data, the development of alternative approaches to research including critical software studies, digital studies, and critical media studies, and development of clear and concise policy recommendations for harmonising CSS approaches with GDPR guidelines in order to encourage and facilitate such studies.


Social sciences have not yet fully embraced the breakthrough of computational science that took place in past years with costs for data transport and data storage ceasing to be a limiting factor for data-driven social science. Developing new crosscutting tools of social and computational science will indeed contribute to better understanding how the EU society acts.

At the same time, although big data (including personal data) has become widespread and minable, datasets available to researchers for scientific enquiry are not so easily available, only under restricted regimes, or they vary in quality. Another important limitation to using these datasets is the respect of data protection regulations put in place by the European Union legislation (for instance GDPR). With CSS it could be critically examined where there is need for more data access to what kind of data, and also where there is not enough high-quality data at all. Proposals are therefore expected to propose new strategies and approaches on how to deal with data, and the lack thereof, in a way that fully complies with the EU’s notion of privacy and personal data.

A promising avenue in this respect is the creation and use for research of synthetic data sets, including full-scale synthetic reference populations. Those can link, while not interfering with personal data use restrictions, highly granular data set. As a result, empirical analysis can much better cater for distributional impacts across a wide range of types of households, and individual socio-economic backgrounds, and the impact of socio-economic policies in different geographical settings can be studied at the same level of detail as currently the case in environmental studies.

Thematically, proposals may choose whichever research focus, in the area of democracy, deemed relevant to exploit the potential of CCS. They may concentrate on testing age-old questions of political economy and political sociology and see how they change or survive when tested in a highly granular simulation environment, as synthetic population data allows to do, or they may identify more recent topics such as political communication, political participation, or resilience of democracies, in relation to structural socio-economic patterns. They may also do methodological research with access to new data sources, develop new methods, or refine existing ones, like social network analysis.

Concrete efforts should be made to ensure that the data produced in the context of the funded projects is FAIR (Findable, Accessible, Interoperable and Re-usable), particularly in the context of real-time data feeds, exploring workflows that can provide “FAIR-by-design” data, i.e., data that is FAIR from its generation. Proposals should leverage the data and services available through European Research Infrastructures federated under the European Open Science Cloud, as well as data from relevant Data Spaces in the data-driven analyses. Additionally, efforts should be made to increase the data availability in European Research Infrastructures federated under the European Open Science Cloud by depositing generated data in relevant infrastructures.

Clustering and cooperation with other selected projects under this topic and other relevant projects are strongly encouraged.

Proposals are encouraged to collaborate with the JRC and its Centre for Advance Studies and project on Computational Social Science for Policy.

[1] Computational Social Science (CSS) uses methods developed in statistical physics to take advantage of the very rich big data sets and identify systematic patterns to deliver new forms of testing hypothesis at comparably low costs.

Application date
Humanities : Anthropology & Ethnology, Digital humanities and big data
Social sciences : Geography, Management and Public administration, International Relations, Political science, Information and Communication Sciences, Sociology
Other : Computer science