Jake S. Truscott

Jake S. Truscott

Assistant Professor
Dept. of Political Science
University of Florida

Public Responses to Supreme Court Leak on Twitter

1 minute read

Published:

Below are a small collection of graphics illustrating unique features of the online political discourse that emerged in the wake of a leaked draft opinion in Dobbs v. Jackson, which would effectively overrule the Court’s landmark opinion in Roe v. Wade (1973). A few notes:

  1. The degree of discourse online regarding the Court increases dramatically when the leak is announced by Politico.
  2. Measuring the rhetorical sentiments of the tweets using a Naive Bayes framework reveals sharp divisions, whereby the distribution of 50,000 tweets leans negative but is overall distributed almost entirely normally.
  3. Differentiating the responses by considering a user’s ideological preference (derived using a Network IRT framework) does not help to explain the variation. Instead, it appears that there is likely a health degree of inter-ideological rhetoric that is both positive(Dem)/negative(Rep) in regard to the leak, as well as for the opinion itself (Positive = Likely Rep, Negative = Likely Dem).
  4. I draw my inference in (3) from an analysis of the common hashtags relayed in susbet of liberal users. Their tweets greatly imply pro-choice rhetoric – e.g., “abortionishealthcare”, “bansoffourbodies”, “abortionrights”, among others.

Tweets Mentioning "SCOTUS" or "Abortion" - 48 Hour Period (May 2-3)

Dobbs1

Tweets Mentioning "Alito" or "Abortion" (April 30 - May 2)

Dobbs2

Tweet Sentiment Classifications Conditioned on Ideological Preferences of User

Dobbs3

Distribution of Tweet Sentiments - Tweets Mentioning "Abortion", "SCOTUS", or "Dobbs" (May 2-4)

Dobbs4

Most Common Hashtags Found Among Liberal Twitter Users - Re: SCOTUS Leak

Dobbs5

You May Also Enjoy

Transcripts of Ketanji Brown Jackson Hearing

less than 1 minute read

Published:

Attached are transcriptions for the recent Supreme Court confirmation hearing of Judge Ketanji Brown Jackson (D.C. Circuit), which are available in (.rdata) format. It was constructed using C-SPAN’s (rough) closed captioning transcriptions of the hearing’s proceedings, which I was able to access by referencing the relevant hyperlink for each portion of the hearing and converting it to a plain text (.txt) file.

Timelapse of Supreme Court Majority Coalitions and Opinion Authorship

less than 1 minute read

Published:

Below is a bar chart illustrating the propensity for Supreme Court justices to join majority opinion coalitions during their tenures between 1791 and 2020. Each iteration represents the number of times a justice joined a majority and ends with their termination from the Court. The second video illustrates the same concept but focuses on the propensity for justices to serve as majority opinion authors. Each were created using Python (bar_chart_race) and replication materials can be provided on request.

An Optimally Classified Supreme Court: Teaching Optimal Classification as an Introduction to Models of Spatial Voting

1 minute read

Published:

This work provides an overview of optimal classification, a rank-ordered scaling procedure that can be used for voters in any parliamentary voting environment. Originally developed by Keith Poole as a computational approach to scaling members of the US Congress (see Spatial Models of Parliamentary Voting 2005), I translate the approach to scaling justices of the Supreme Court. At its core, optimal classification in a single dimension uses a cutting point procedure and a legislative procedure to optimally rank order voters (though it can theoretically be applied to more than one dimension). The benefits of the approach are found largely in its ease of implementation. Unlike statistical approaches (e.g., NOMINATE or Martin-Quinn Scores), the procedure can be completed with pen and paper if the number of votes and voters is small enough. However, using a computational approach (as opposed to statistical) means that the rank ordering will not produce cardinal distances - merely an optimal rank ordering. I ultimately address how the procedure can serve as a good tool for introducing students to models of spatial voting.