Issue attention on international courts: Evidence from the European Court of Justice

Abstract

We exploit variations in access rules on the European Court of Justice to explore the eect of procedural inclusiveness on the agenda of international adjudicators. Using natural language processing methods, we analyse the entire universe of ECJ decisions up to 2015, mapping issue prevalence across time, procedures and litigant type. We nd evidence that the more inclusive annulment and referral procedures are associated with greater issue heterogeneity whereas less inclusive infringement procedure displays greater issue cohesiveness as well as greater issue stability over time.

Analysing EU Treaty-Making and Litigation With Network Analysis and Natural Language Processing

“…International agreements seem to have become increasingly less relevant in cases pertaining to public procurement ("contracts, tender, award, consumer") and indirect taxation ("vat, tax, sixth, taxable"). These legal areas, along with trademarks ("mark, trademark, euipo, board") and road safety ("insurance, vehicles, vehicle, freedom") have seen increasing regulatory harmonization at EU level; a development that seems to been accompanied by intensifying litigation [7]. To the extent that these topics are highly prevalent in recent years, they may explain the pattern seen in Figure 2, which shows a declining proportion of EU court citing EU international agreements.…”

“…Our paper relates to the growing literature applying machine learning and Natural Language Processing (NLP) methods to the study of law and legal documents [5][6][7]. It also relates to the legal and political science literature applying network analysis methods to the analysis of case citation dynamics [8][9][10][11] as well as the evolution and structure of legislation and networks of judges and law professors [12][13][14].…”

Facilitating access to data on European Union laws

“…Given the size of the database, the data are particularly interesting for quantitative text analysis. There is a vast array of quantitative text tools, but topic modelling is a common use case (Greene and Cross 2017;Dyevre and Lampach 2021). I use latent Dirichlet allocation (Blei, Ng, and Jordan 2003) to classify the text of (non-implementing) directives into twelve topics.…”

Text-mining for Lawyers: How Machine Learning Techniques Can Advance our Understanding of Legal Discourse

“…Recent work has applied topic modelling to study relative issue emphasis across infringement, annulment and preliminary rulings, highlighting how the CJEU's case law is influenced by the litigation agenda of case initiators (like the European Commission); 16 to compare topic salience in European Union legislation, CJEU rulings and contributions to the Common Market Law Review; 17 to explore Dutch Supreme Court decisions; 18 and to demonstrate the lingering centrality of market regulation in European Union law-making in the twenty-first century. 19 While significant efforts have been expended on manually classifying the legal areas addressed by US Supreme Court rulings, some authors have proposed topic modelling as a more efficient and more accurate alternative.…”

“…For a review see J. Frankenreiter and M.A. Livermore, 'Computational Methods in Legal Analysis', 16 Annual Review of Law and Social Science 39-57 (2020); for reflections and illustrations of the use of machine learning and natural language processing methods in empirical legal studies see M.A. Livermore and D.N.…”