From Modeled Topics to Areas of Law

Quantitative approaches are gaining popularity in German legal research. The analysis of large corpora of legal text may be supported by text mining methods. In this study, we employ topic modeling—which aims at retrieving the “topics” of a corpus—to identify words related to certain areas of law present in the case law of the German Federal Constitutional Court (FCC). This information is then evaluated by legal experts and used to show significant content-related differences between the two most frequent types of proceedings before the FCC. Technical and somewhat unstable areas of law, such as tax law, social law, and civil service law, are significantly overrepresented in referrals for judicial review, whereas areas of law characterized by well-developed case law and judicial doctrine appear substantially more often in constitutional complaints. This insight may come as a surprise due to the fact that the Court’s material scope of review is identical in both types of proceedings. Our considerations do not, however, seem to apply to private law. Though we recognize the methodological and epistemological concerns regarding the heuristic nature of topic modeling, this study exemplifies its productive use in complementing, rather than replacing, more traditional techniques of analysis in legal studies

Common law · Constitution · Constitutional court · Doctrine · German · Legal doctrine · Legal history · Linguistics · Political science · Popularity · Private law · Public law · Sociology · Surprise · Artificial Intelligence in Law · Comparative and International Law Studies · Computer Science · Law