Oxford Computational Political Science Group

Research Programme:
OCPSG aims to transform Oxford into a research hub for computational political science by fostering interdisciplinary collaboration and offering first-hand research opportunities. Our Research Programme works on replications of existing research, visualizations of key political science topics, critiques of methodologies or computational method applications of existing research, and also original research projects.

Our Research Programme features students and young researchers holding degrees from or currently studying at Oxford (Department of Politics and International Relations, Oxford Internet Institute, Oxford School of Global and Area Studies, School of Geography and the Environment) or in other leading universities, such as Cambridge, LSE, UCL, KCL, QMUL, Edinburgh, Essex, Yale, Chicago, UC Berkeley, UC San Diego, Penn State, Leiden, HKU, and CUHK. Their backgrounds range from political science to international relations, public policy, geography, law, public health, biology, history, Chinese studies, engineering, statistics, mathematics, sociology, philosophy, economics, and data science.

• Project 1, led by Bastián González-Bustamante (Leiden), focuses on benchmarking zero-, few-shot and reasoning LLMs’ capabilities against fine-tuned Transformer baselines for multilingual policy agenda annotation using European parliamentary speeches as the core corpus. It aims to construct a reproducible evaluation pipeline that compares open and closed LLMs with fine-tuned BERT-family models across multiple European languages, quantifying trade-offs in performance, robustness, computational costs, and carbon footprint.

   

• Project 2, led by Daria Godorozha (LSE) and Sara Gabrielli (LSE), focuses on applying natural language processing (NLP) and sentiment analysis to examine how Migration Law affects and intersects with religious Anti-Discrimination Law across the EU and UK, both in theory and in practice.

   

• Project 3, led by Stephan Wenninger (LSE), focuses on examining the past and future effects of wildfire activity on climate opinion in the US at the county level. Besides employing statistical inference that allows for cross-sectional and time-sensitive heterogeneity, it also tests different machine learning algorithms to create a forecast of future changes in climate opinion. 

   

• Project 4, led by Ryan Ratnam (Oxford / Penn State), focuses on examining the changing structures of Manosphere communities over a longitudinal period. It employs social network analysis to map Manosphere communities, utilizes survival analysis to predict how long users stay active and the probability of user migration, and leverages interrupted time-series analysis to infer the effects of offline events on the Manosphere’s structure.

   

• Project 5, led by Maria Milosh (Chicago / MetaGov), focuses on investigating how exposure to others’ reasoning shapes preferences and collective decisions in large-scale online policy discussions. It also employs text analysis to identify policy and well-being arguments in transcripts of collective deliberations, trace their evolution, and measure how participants’ views shift once they engage with others’ perspectives.

   

• Project 6, a joint collaboration with Europinion − Europinion Institute for Research (EIR), focuses on exploring how disinformation networks linked to Russian influence campaigns affect public trust and democratic resilience within the European Union. This project employs automated text analysis to examine how Russian disinformation efforts interact with domestic and European political narratives in EU democracies, shaping public trust and institutional resilience.

• Project 1, led by Thomas Brailey, focuses on developing a fine-grained geospatial measure of coercive state capacity. It combines geolocated survey data, geospatial data, and road data. Using this measure of military, police, and security apparatus reach, this project investigates the relationship between state capacity and various outcomes like attitudes toward violence and instances of private violence.

   

• Project 2, led by Calvin Cheng, focuses on addressing key gaps in current research on the moral contagion effect. It aims to test this effect within the context of online information diffusion. It focuses on developing automated methods to measure morality in textual data and investigating how moral content spreads and influences behaviour on social media, using innovative causal research designs to account for confounding factors. Read the summary of their project output.

   

• Project 3, led by Zihao Zhang, focuses on working on mapping the political communication landscape in simplified Chinese on foreign platforms. It focuses on discovering ideological categories in politics-related posts and classifying key opinion leaders (KOLs) based on their posting contents using natural language processing techniques and mapping the information networks of KOLs based on common followers using social network analysis.