Legal scholars study international courts by analyzing only a fraction of available material, which leaves doubts as to whether their accounts correctly capture the dynamics of international law. In this paper we use dynamic topic modeling, a family of unsupervised machine learning techniques, to gauge the shifts in the content of the case-law of international courts over longer time spans. Our results indicate that dynamic topic modeling is a powerful and reliable tool to systematically and accurately track legal change over time and enhance our understanding of courts and their influence on the law.
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