I received a Ph.D. in Political Science from New York University in May 2015, having specialized in the subfields of Comparative Politics and Quantitative Methods. I am currently a Research Fellow and Associate Lecturer in the Q-Step Centre at the University of Exeter, where I am part of two ESRC-funded projects: the NCRM Methodological Innovation grant “ExpoNET: Measuring Information Exposure in Dynamic and Dependent Networks” and “Media in Context and the 2015 General Election: How Traditional and Social Media Shape Elections and Governing”. I am also responsible for designing and delivering quantitative data analysis lab sessions for a number of Q-Step modules, such as Data Analysis for the Social Sciences I and II, Economics of Politics, Money and Politics in the United States and Political Psychology. Before coming to Exeter, I have spent the last year of my Ph.D. as a Fellow in the Department of Methodology at the London School of Economics and Political Science, where I have completed my dissertation and taught postgraduate level classes in Applied Regression Analysis and Social Science Research Design, as well as an Introduction to Text Analysis in Python workshop.
In my work I rely on a range of quantitative methods such as natural language processing and quantitative text analysis, basic machine learning algorithms, survey experiments and agent-based modelling, in order to study electoral competition, campaigns and social media and political participation. In my Ph.D. thesis I documented the existence of partisan perceptual bias effects among political actors. Using a survey of U.S. Congress candidates that I conducted in 2012, together with natural language processing and quantitative text analysis of their social media posts, I showed that similarly to voters, candidates have biased perceptions of the political environment as well as of other political actors' ideological positions and that candidate bias is positively associated with the level of district competition. Biases in candidate perceptions of their opponents’ positions are higher for candidates who use more competitive language in their campaign tweets and adopt a more confrontational attitude towards their opponents and the opponents’ parties. To measure the level of competitive language in candidate tweets, I programmed and implemented an automated dictionary-building method which is widely applicable and can be used to generate dictionaries for any concept of interest, with minimal initial human input. The method was validated through crowd-sourced human coding, using CrowdFlower and Amazon MTurk coders. Finally, I focused on the causal link between biased perceptions and competition and conducted an online cross-national survey experiment to show that partisan perceptual bias effects are universal and can emerge naturally, as a psychological response to political competition.