Michele Scotto di Vettimo

Michele Scotto di Vettimo

Postdoctoral Research Fellow

University of Exeter

About me

I am a Postdoctoral Research Fellow in the Department of Social and Political Sciences at the University of Exeter, where I work on the OPTED (Observatory for Political Texts in European Democracies) and PRECEDE (Populism’s Roots: Economic and Cultural Explanations in Democracies of Europe) projects.

I obtained my PhD in Political Science from the King’s College London, and hold a BA in Political Science and master’s degrees in Management of Public Administrations and European Affairs. I’m also member of the Italian Political Science Association (SISP).

I study public opinion - particularly public support for EU integration - and the responsiveness of non-majoritarian institutions and of the EU policy-making process. I am particularly interested in methodologies like quantitative text analysis, machine learning algorithms and item response approaches.

Interests
  • Comparative politics
  • Political economy
  • European Union
  • Public opinion
  • Quantitative text analysis
Education

  • PhD in Political Science, 2022

    King's College, London

  • MSc in Management of Public Administrations, 2016

    Bocconi University, Milan

  • MA in European Affairs, 2016

    SciencesPo, Paris

  • BA in Political Science, 2014

    University of Naples

Publications

Christel Koop, Michele Scotto di Vettimo (2023). Non-majoritarian institutions, media coverage, and 'reinforced accountability'. Forthcoming in Governance.

Article Data Blogpost

Christel Koop, Michele Scotto di Vettimo (2023). Do the media scrutinise central banking? Evidence from the Bank of England. European Journal of Political Economy, 77, 102296.

Article Data Blogpost

Daniel Devine, Jessica Smith, Viktor Valgarðsson, Will Jennings, Michele Scotto di Vettimo, Hannah Bunting, Lawrence McKay (2023). Political trust in the first year of the COVID-19 pandemic: A meta-analysis of 67 studies. Forthcoming in Journal of European Public Policy.

Article

Michele Scotto di Vettimo (2022). Measuring public support for European integration using a Bayesian IRT model. European Union Politics, 23(2): 171-191.

Article Data Blogpost

Working papers

If you would like to get access to the latest version of a paper, feel free to send me an e-mail.

Research Challenges and Training Needs in Text Analysis for Social Science Research
Italian-style Putinverstehen: Support for Putin among Italian voters
Do central bank communication and outreach affect public attitudes towards the organisation? Evidence from a survey experiment
The politics of central banking: Using text as data to analyse technocratic responsiveness
Multi-level politics in action: How national elections make European policies more responsive

Projects

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All European Union Public opinion Political texts

Tracking Euroscepticism (across time and space)

This post aims at tracking the level of support for European integration over a very long time frame in all the member states of the European Union. It covers the overall support for the European Union and to the support for European integration in the areas of asylum and immigration policies, fiscal and economic affairs, internal market and consumer policies; and social and welfare policies.

A survey of the research challenges and training needs in text analysis for political science research

This online survey assessed the research challenges, training needs and preferences of researchers and practitioners (of all seniority and skills level) that use or are interested in using text analysis techniques in their research. The survey has been conducted within the context of the Horizon 2020 OPTED project.

Data Documentation

Automated web scraping of EU legislation

This project presents an easy to use application to gather key data and textual summaries of EU legislative proposals from the European Parliament’s Legislative Observatory and EUR-Lex websites. All the information is stored in spreadsheets and can be quickly imported in any software for statistical analysis as well as quantitave text analysis.

Download Documentation

Teaching

University of Exeter, Department of Social and Political Sciences                                            

Teaching Assistant (2023)

  • Data Analysis for Social Sciences, Undergraduate (module page)

Dissertation supervisor, Undergraduate (2022-2023)

King’s College London, School of Politics and Economics

Teaching Assistant (2018-2021)

  • Introduction to Political Sciences, Undergraduate
  • Comparative Political Systems, Undergraduate
  • Integration of the European Union, Undergraduate     

Contacts