European Strategies in the Shadow of Sino-American Competition: A Text-as-Data Approach
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Authors
Becker, Jordan
Jee, Haemin
Budanu, Andreea
Love, Maxwell
Benson, Seth
Issue Date
2024-03-13
Type
Conference presentations, papers, posters
Language
Keywords
Machine Learning , Text as Data , Strategy , China , United States , Europe , Foreign Policy
Alternative Title
Abstract
As Sino-American competition becomes a key factor in structuring 21st century international relations, researchers and policymakers are interested in how third states align in relation to China and the US. So far, research on this topic has been qualitative–scholars have speculated as to the alignment of various actors vis-à-vis China and the United States, but no analysis has systematically arranged and compared a group of states or offered a consistent set of measurements for alignment. This limitation impedes replicability and generalizability of analyses. We introduce a dataset that uses text as data to systematize discursive alignment of up to 34 European states for as many as 50 years, using two different automated content analysis techniques. In more recent years (since 2014), we focus on alignment specifically regarding China’s “Belt and Road Initiative.” We discuss the main features of this data in the paper, and the replication files will enable other scholars to build on our work in the future. In addition to automating the extraction of quantitative sentiments from key documents, we make our library of documents available for other researchers to analyze along other dimensions of interest to them. We illustrate the utility of the dataset by describing differences across countries and over time. By focusing on European states, we shed light on Europe’s relationship with both China and the United States, as well as the concept of European strategic autonomy.
Description
Citation
Becker, J., Jee, H., Budeanu, A., Love, M., & Benson, S. (2024). European Strategies in the Shadow of Sino-American Competition: A Text-as-Data Approach - Conference Paper, United States Military Academy, West Point, 7 February 2024 (SSRN Scholarly Paper 4758032). https://papers.ssrn.com/abstract=4758032
Publisher
n/a