Project Lead: Jihyeon Bae, Ph.D. Candidate, Political Science
Data Science Lead: Valentina Staneva
Every year, more than 200 state representatives open the United Nations General Assembly meeting with statements during the General Debate (UNGD). The UNGD speech floor provides a rare opportunity for all states to equally have their voices heard by a wide range of audiences. In addition, UNGD statements are crucial factors that set the agenda for the remaining sessions and incentivize states to condense their viewpoints concisely. This project analyzes a large text corpus containing 10,568 English transcripts of speeches delivered by state representatives of UN member states at the United Nations General Debate from 1946 until 2022.
In the first stage, we pose a testable hypothesis: “How do democracies and autocracies frame the principle of sovereignty differently?” Sovereignty is the most fundamental legal principle in the realm of global governance, developed to guarantee legally equal status among states and respect authority over territories. However, authoritarian states have invoked the sovereignty principle, framing it as a free pass to enact any policies domestically. We aim to determine if there is any systematic difference in rhetorical usage between the two types of regimes, using text analysis models. We use pre-trained static and dynamic models like GloVe and BERT to generate word-embeddings for each document.
In the next stage, we analyze not only what the leaders say, but how they speak by employing computational linguistics models. Our goal is to unpack the preferences of authoritarian state leaders by mapping UNGD data to psychological markers. Linguistic Inquiry and Word Count (LIWC) generates dictionary-based measures of constructs that tap into linguistic styles. Using simple regression and random forest model, our prediction model hit 75% accuracy level of predicting regime type using linguistic features. This project is expected to contribute to the timely discussion on the growing political clout of authoritarian regimes.