The Wide Arc of the Supreme Court

The Supreme Court now hears around 70 arguments a term and each case tends to have issues unique from others on the Court’s docket.  After the Court’s merits docket is assembled each term though, similarities between cases become apparent and these similarities may present an area of law where the Court is more invested and wants greater resolution. Such areas can be either broad based or narrow.

Last term the Court settled multiple issues surrounding the First Amendment. The Court resolved public-sector employees’ First Amendment rights regarding agency fees in Janus, First Amendment retaliatory arrest claims in Lozman, acceptable political apparel at polling places in Mansky, and analyzed the compatibility of California Reproductive Freedom, Accountability, Comprehensive Care, and Transparency Act with the First Amendment in Becerra.  It also worked through the free exercise First Amendment question in Masterpiece Cakeshop, while it left the free speech component of the case unanswered.

During the 2016 term the Court worked through several issues relating to patent law in the cases Samsung v. Apple, SCA Hygiene, Life Technologies, Sandoz, Impressions Products, and TC Heartland. Although there was a dropoff in such cases for the 2017, the Court hard several salient patent cases last term as well with Oil States, SAS Institute, WesternGeco.

What will the Court hear this term?  According to Professor Rory Little, the Court’s merits docket is currently filled with criminal law cases.  That is not all though. While the Court’s argument calendar currently lacks blockbuster cases, certain patterns are evident in the types of cases the Court has already accepted for oral argument.

One of the most interesting ways to locate underlying patterns without significant prior information about the contents is through unsupervised learning and more specifically through topic modeling. Empirically minded legal academics have used topic models to gauge issues before the Court in the past. In a recent paper, Professors Michael Livermore, Dan Rockmore, and Allen Riddel used topic models to longitudinally study agenda formation in Supreme Court.  Professor Douglas Rice used topic models in an article looking at differences in framing between Supreme Court majority and dissenting opinions.  In another article, Professors Benjamin Lauderdale and Tom Clark used topic models along with other methodological tools to highlight issues in Supreme Court cases along with the justices’ preferences based on case issues.  This post takes a cut at the Court’s recent cases to see what the justices’ have examined in the past several terms as well as what they will review this term.

This is an interesting and dramatic time for the Supreme Court.  While the expectations ran high for a conservative agenda this term based upon Kennedy’s departure, now the reality is setting in that the Court might be with eight justices for at least the near future and so the conservative agenda might be shelved, if only momentarily.  The public expects the Court to handle cases with significant impact in the near future although the majority of the public’s position is not always aligned with that of the majority of the justices.  The uncertainty about the ninth seat on the Court has in any case left the Court without cases dealing with hot-button issues like abortion or voting rights.

Although one of the key aspects surrounding topic models is that they don’t require much prior substantive knowledge of the area under exploration, one setting that users input is the number of issues that the algorithm will uncover. This number could be arbitrary but is usually either based on some expectation or can be narrowed through a process of iterating the algorithm several times with different parameters for the number of topics.


The texts I used to generate the topics were each case’s question or questions for the Court as contained on the Supreme Court’s website (here is an example from last year’s case Jennings v. Rodriguez).  Although the estimate of topics is rough at best, I set the number of issues for cases between 2013 and 2017 to 25 and the number of words per topic to six (although sometimes the number of words is greater than six if there are equally weighted words).  The words in the topics were stemmed so that words with the same root were combined (for example tell, tells, and telling would be grouped together).  The topics were derived as follows:


When evaluating topics, notice that the beta values or relative level of significance below each topic are on different scales. The larger the number in these scales indicates a greater importance of a term to a topic and consequently the presumed importance of the term to cases that fall within that topic.

Some of the topics can clearly be associated with case categories. Topic 5, for instance, focuses on patent cases, Topic 13 on qualified immunity, Topic 17 on bankruptcy, and Topic 20 on criminal sentences. The cohesion between terms in other topics is vaguer.  Topic 22 for example likely deals with campaign contributions and 21 on statutes with differential race-based effects.  Topic 1 might relate to the review of state statutes but also might be more in line with rules of federalism.  Topic 9 likely has some relationship to employment discrimination although this is still not entirely clear based on the words alone.

We can get a better sense of the connective glue between terms in a topic by matching cases with their top topics (cases are weighted into multiple topics but for the purpose of this analysis only the top ranked topic per case was retained).  This also allows us to differentiate cases by their underlying issues as well as group similar cases together.The next figure is an exhaustive list of 320 argued cases between the 2013 and 2017 terms along with each case’s top issue.

With topics and cases in hand, we can begin the process of matching cases to topics and interpreting how and why cases fit into their respective topics.

An example from each of the Court’s terms between 2013 and 2017 will help illustrate this interpretive technique.  During the 2013 term, the Court decided McCullen v. Coakley,which dealt with abortion protester buffer zones.  The case’s top topic was 18 with the word stems: statut, enforc, provid, articl, base, individu, resolv, and public. While the link between these terms and abortion clinics may not have been clear at the outset, more clarity is attained with a little information about this case (even the modicum provided above) as it relates to this topic.

In the 2014 term the Court heard the employment discrimination case Young v. United Parcel Service. The case specifically looked at disparate treatment of pregnant women where some but not others were afforded accommodations.  This case fell under Topic 7 with the term stems: appeal, issu, provid, determin, clear, and offici.  While these stems do not clearly indicate that they relate to employment discrimination on their face, when related to this case the relationship becomes evident.

The Court heard the standing related case, Spokeo v. Robins, during its 2015 term.  Spokeo, a case looking at when parties have standing to bring a case, fell under Topic 9.  Topic 9 looks at requirements and elements which both fit well with questions of standing. Spokeo also looked at the enforcement of rights between private parties.  Based on the topic terms Spokeo is properly categorized under Topic 9.

During the 2016 term the Court heard the criminal sentencing case Beckles v. U.S.  Beckles’ main topic is number 20, which among other things includes sentences, decisions, retroactivity, guidelines, and minimum. These are all very important words in relationship to Beckles which specifically dealt with the Federal Sentencing Guidelines.

Last term the Court heard the case Gill v. Whitford. Although the substantive questions in the case remained largely untouched, the gerrymandering case which fell under Topic 21 dealt with election districts, voting plans, and violations of the First and Fourteenth Amendments.

Cases are not isolated to one topic though, and topics do not always clearly establish the main issues in cases. Take last term’s case Carpenter v. U.S. Carpenter’s main topic is 7. Carpenter dealt with Fourth Amendment protections against warrantless searches of cell tower records. Topic 7 does not overtly describe these facets with its terms. Carpenter most likely falls into a mix of several topics with no topic in particular dominating its composition. Based on this analysis, not all case issues are going to be clarified by the main topics although some cases will be described much better than others.


For the 2018 term I examined the questions presented in 42 petitions related to arguments that the Court will or already did hear.  Since this sample is more limited than the larger sample of cases between the 2013 and 2017 terms, I limited the number of topics to seven with seven terms per topic (unless multiple terms had equal weights).


Topic 1 clearly deals with capital punishment and Topic 2 relates to arbitration agreements. The cohesion between words in other topics either less clear or to include a mix of issues. The cases that fall under certain topics may also provide the connective glue between words in the topic.

Since the number of cases for the 2018 term was more manageable with 42, I included the relative weights of each topic for each case as is displayed below.


Some cases bear strong relationships to one topic while others to multiple topics.  We see that 17-8151, Bucklew v. Precythe, and 17-7505, Madison v. Alabama, both death penalty cases, have Topic 1 as their main topics. Bucklew has a greater relationship to Topic 1 though than Madison.

Azar v. Allina Health Services looks at whether the Department of Health and Human Services is required to follow certain procedures before providing challenged instructions to Medicare contractors. The case, number 17-1484 is predominately under Topic 2 which looks at employers and requirements although doesn’t examine other terms under the topic like design and immunity. Number 17-5554, Stokeling v. U.S., a criminal case, also falls mainly under Topic 2. This case looks at requirements and elements of “violent felonies” but has little to do with other aspects of the topic like design and arbitration.

Case number 17-71, Weyerhaeuser Company v. U.S. Fish and Wildlife Service, which was recently argued before the Court is mainly under Topic 5.  This topic covers many of the essential aspects of the question in this case like critical, habitats, and land, although several of the other words like author, instruct, and career are unrelated to the case’s question.

Not all cases are classified equally well by these topics and some cases are better categorized by multiple topics rather than by just one. Looking at the different sets of topics we can get a sense of the cases the Court heard or will hear based on words related to the cases’ main questions.  We can also look at the set of topics to give a sense of the terrain of cases before the Court.  Several of the topics for the 2018 term have words related to criminal law. Others focus on arbitration and employment which is another of the Court’s focal points early in this term. Other, less apparent aspects of this term’s cases are also brought to light with these topics. Topic 3 for example focuses on foreign, nation, and states, all which are components of the questions in Jam v. Int’l Finance Corp. and Republic of Sudan v. Harrison.

This method for examining case issues will not perfectly locate all issues before the Court. It will, however, provide a good summary of important issues and especially of ways to bracket multiple cases under similar topics. It may also illuminate new ways of categorizing cases that were previously undetected through qualitative analysis alone.

On Twitter @AdamSFeldman

Helping lawyers make efficient use of data @ Optimized Legal

15 Comments Add yours

  1. Roxanne Friedman says:

    I haven’t looked at topic modeling at all, but it seems to bear a lot of resemblance to factor analysis, perhaps with a logistic model in the background. If this is the case, then the “correct” number of topics should be determinable by looking at the number of topics with eigenvalues >1, that is, providing at least as much discriminatory power as knowing the case. Also it would allow for “rotations” in which the terms with the most discriminatory power were concentrated in separate topics, while those with the least power (perhaps arising from the formalism of writing a question presented) are spread across many topics. This would ease the interpretation of topics.


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