# Understanding State Preferences With Text As Data: Introducing the UN General Debate Corpus

arXiv: https://arxiv.org/abs/1707.02774 · HTML: https://mlsanity.com/paper/1707.02774/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2017.

## Built on by (most cited first, 11 of 11)

- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset](https://mlsanity.com/paper/2207.00220/) (2022)
- [Goal Driven Discovery of Distributional Differences via Language Descriptions](https://mlsanity.com/paper/2302.14233/) (2023)
- [Modeling Dynamic Topics in Chain-Free Fashion by Evolution-Tracking Contrastive Learning and Unassociated Word Exclusion](https://mlsanity.com/paper/2405.17957/) (2024)
- [Neural Dynamic Focused Topic Model](https://mlsanity.com/paper/2301.10988/) (2023)
- [Towards Region-aware Bias Evaluation Metrics](https://mlsanity.com/paper/2406.16152/) (2024)
- [RooseBERT: A New Deal For Political Language Modelling](https://mlsanity.com/paper/2508.03250/) (2025)
- [Visualizing Temporal Topic Embeddings with a Compass](https://mlsanity.com/paper/2409.10649/) (2024)
- [Cognitive Insights and Stable Coalition Matching for Fostering Multi-Agent Cooperation](https://mlsanity.com/paper/2405.18044/) (2024)
- [Mining United Nations General Assembly Debates](https://mlsanity.com/paper/2406.13553/) (2024)
- [Zero-Shot Visualization: Exploring Text Corpora with User-Prompted Axes](https://mlsanity.com/paper/2610.06889/) (2026)

## Abstract (arXiv)

Every year at the United Nations, member states deliver statements during the General Debate discussing major issues in world politics. These speeches provide invaluable information on governments' perspectives and preferences on a wide range of issues, but have largely been overlooked in the study of international politics. This paper introduces a new dataset consisting of over 7,701 English-language country statements from 1970-2016. We demonstrate how the UN General Debate Corpus (UNGDC) can be used to derive country positions on different policy dimensions using text analytic methods. The paper provides applications of these estimates, demonstrating the contribution the UNGDC can make to the study of international politics.