Book Project

Text Analysis Using R (with Kenneth Benoit).
[Draft Version]

Papers Under Review

Nostalgia in European Politics: A Novel Text-Based Measurement Approach (with Sven-Oliver Proksch).

Citizen Reactions to Experts in Deliberative Assemblies (with Garrett Kennedy and Tomás Maher).

Journal Impact Factor and Peer Review Thoroughness and Helpfulness: A Supervised Machine Learning Study (with Anna Severin, Michaela Strinzel, Matthias Egger, Tiago Barros, Alexander Sokolov, and Julia Vilstrup Mouatt).
[PDF] [Nature Q&A]

How Slack Facilitates Communication and Collaboration in Seminars and Project-Based Modules.
[PDF]

Working Papers

Discourse Wars and ‘Mask Diplomacy’: China’s Global Image Management in Times of Crisis (with Samuel Brazys and Alexander Dukalskis).
[PDF]

Campaign Promises and Legislative Leadership Posts (with Naofumi Fujimura).

Posting Political Proficiency: How Candidates Use Social Media to Signal Their Qualities During Election Campaigns (with James P Cross, Derek Greene, and Martijn Schoonvelde).

Legislating Landlords: Private Interests, Issue Emphasis, and Policy Positions (with Jihed Ncib).

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

Analysing Grant Peer Review Reports Using Machine Learning

Project Summary: Peer review plays an essential role in grant evaluation. External peer review reports by international experts contribute to assessing the feasibility and quality of grant applications and provide an essential basis for funding decisions. In addition, they help justify rejections and provide feedback, which may help applicants improve their research. Peer review thus has the power to influence which researchers and what kind of research receives funding and can subsequently be conducted. For funding organisations, peer review must fulfil these functions. Peer review reports should also be in line with their understanding of quality. Peer review should also enable fair, transparent, and efficient funding decisions and foster diversity in research (ideas, methodologies, and approaches) and researchers.

This research project is a collaboration between University College Dublin and the Swiss National Science Foundation. The project will analyse the texts of anonymised grant review reports along several dimensions using human coding and machine learning. We seek to conceptualise characteristics of grant peer review reports and classify a large corpus of review reports. The project investigates whether strategic initiatives and new evaluation procedures have the desired effects on the content and structure of review reports.

Project Funding: €276,000

Assessing and Explaining Environmental and Energy Policies in Comparative Perspective

Project Summary: Political parties, politicians, companies, and interest groups increasingly discuss how to achieve a net-zero carbon emissions future, but systematic evidence that tracks these political debates is still lacking. The project “Assessing and Explaining Environmental and Energy Policies in Comparative Perspective” seeks to identify the problems political actors raise and solutions they offer regarding renewable energy, sustainability, and water treatment. The project will also assess how companies and interest groups aim to reduce greenhouse gas emissions and help mitigate the impacts of climate change. By combining quantitative text analysis, human coding, and supervised machine learning, it will define and map (proposed) policies relating to the environment and sustainability, and provide recommendations for policymakers.

The project is part of NexSys, a newly established All Island SFI Strategic Partnership Programme. NexSys focuses on the transition to a net zero carbon energy system. It is a unique partnership bringing together a multidisciplinary research team, industry, and policymakers to tackle fundamental research questions to be addressed as part of the transition to net Zero. Hosted by UCD Energy Institute, NexSys brings together academics from nine institutions across the Island of Ireland (University College Dublin, Trinity College Dublin, Dublin City University, ESRI, Maynooth University, University College Cork, NUI Galway, Ulster University and Queen’s University Belfast) to work together to meet the unprecedented scale and complexity of the challenges associated with the energy transition.

Project Funding: €183,000 (total funding: €16 million)

Module Instructor: Undergraduate Level

• Parties and Party Competition (University College Dublin: Spring 2020, Autumn 2021, Spring 2023).
  [Syllabus]

• Representation and Party Competition (University of Zurich: Autumn 2019, Spring 2020).
  [Syllabus]

• Hope (Discovery Module; coordinated by Imelda Maher, UCD Sutherland School of Law: Spring 2023)

Module Instructor: Postgraduate Level

• Introduction to Statistics (University College Dublin: Autumn 2020, Autumn 2021, Autumn 2022).
  [Syllabus]

• Quantitative Text Analysis (University of Zurich: Spring 2019, Autumn 2019; University College Dublin: Spring 2022, Spring 2023).
  [Syllabus]

• Connected_Politics (University College Dublin; Spring 2021, Spring 2022).
  [Syllabus]

• Political Representation and Policy Preferences (University of Zurich: Autumn 2019, Spring 2020).
  [Syllabus]

I maintain and continuously update a GitHub repository with the syllabi of all modules.

Workshop Instructor

• Quantitative Text Analysis for Beginners: UCD Geary Institute for Public Policy (2022); University of Lucerne (2021); University of Hamburg (2021); Scottish Graduate School of Social Science (2020); University of Bremen (2020, 2018); COMPTEXT (2019: Tokyo, 2020: Innsbruck); Swiss National Science Foundation (2019); Kobe University (2019); University of Düsseldorf (2019); University of Bergen (2019); Trinity College Dublin (2018); WZB Berlin (2018)

• Creating Academic Personal Websites: Geneva Graduate Institute (2022); University of Lucerne (2022); University College Dublin (2020)

• Data Wrangling and Visualisation: University College Dublin (2020); Trinity College Dublin (2017)

• Reproducible Research with Git and GitHub: University College Dublin (2021)

I could also teach these workshops at your institution. Do not hesitate to contact me.

Teaching Qualifications

• Professional Certificate in University Teaching & Learning, University College Dublin (2021–2022)
• Certificate in Academic Teaching & Supporting Learning, Centre for Academic Practice and eLearning, Trinity College Dublin (2016)

Teaching Awards

• Teaching Excellence Award, University College Dublin, College of Social Sciences and Law (2022)
• Teaching and Learning Prize, Political Studies Association of Ireland (2022)
• Dermot McAleese Award for Teaching Excellence, Trinity College Dublin (2018)

Irish Polling Indicator

The Irish Polling Indicator is a joint project with Tom Louwerse. We combine all opinion polls for the Dáil Éireann into daily estimates of public support for Irish parties. We released a new Irish Polling Indicator website and provide datasets containing results from over 660 polls since 1983 and daily aggregated estimates from 1987 to 2022.

UCD Expertise featured the Polling Indicator and related research.

The figure below (updated automatically after the release of new polls) reports the most recent estimates of party support.

Irish Demographic Polling Datasets

The Irish Demographic Polling Datasets collect aggregated results on vote intentions, satisfaction with the government, and popularity of party leaders. The data are available for all respondents and various subsamples, such as age groups, gender, social class, geographic region, and district magnitude. The database merged over 100 polls, published between 2011 and 2022. The data were collected in collaboration with Thomas Pluck.