Network Analysis and the Law: Measuring the Legal Importance of Precedents at the U.S. Supreme Court

Abstract

We construct the complete network of 26,681 majority opinions written by the U.S. Supreme Court and the cases that cite them from 1791 to 2005. We describe a method for using the patterns in citations within and across cases to create importance scores that identify the most legally relevant precedents in the network of Supreme Court law at any given point in time. Our measures are superior to existing network-based alternatives and, for example, offer information regarding case importance not evident in simple citation counts. We also demonstrate the validity of our measures by showing that they are strongly correlated with the future citation behavior of state courts, the U.S. Courts of Appeals, and the U.S. Supreme Court. In so doing, we show that network analysis is a viable way of measuring how central a case is to law at the Court and suggest that it can be used to measure other legal concepts.

Random graph models for directed acyclic networks

“…The best-studied examples are citation networks, networks in which the vertices represent documents and the directed edges represent citations between them. Citation networks of learned papers have long been an object of study in the information sciences [1,2,3] and more recently in physics [4,5], and citation networks of patents [6] and legal cases [7,8] have also received some attention in the last few years. Directed acyclic graphs occur in many other areas too.…”

Priority Queuing on the Docket: Universality of Judicial Dispute Resolution Timing

“…Much work has focused on judicial opinions and the structure of the precedent citation network. For instance, Fowler and colleagues explored opinion centrality and how it relates to an opinion's perceived importance [1,2]. Building on this work, Whalen et al examine which backwards citation patterns relate to an opinion's future forward citation centrality [3] and the general trend toward an increase in network-focused legal studies [4].…”

Law and Legal Science in the Age of Big Data

“…These 'text-empirical research projects' can be subdivided into two types: projects that are based on human reading and coding (See for example, the famous Supreme Court (Spaeth) database, 9 or the European Conreason project 10 and projects based on machine processing (see e.g. Fowler et al, 2007;Ződi, 2015). In the case of the manually coded research, it is quite clear that coding sometimes requires interpretation and partially arbitrary decisions, but even in the case of a machine-made analysis during the construction of the text-analysing algorithms, one must make certain decisions which can distort the data collection itself.…”