Hannah Garringer studied sociology at Wheaton College and graduated in 2018. She hopes to pursue further education in the field of computational social science. Hannah is primarily interested in studying groups that possess power based on racial or religious social markers, as well as how the language used in a courtroom interacts with existing biases to affect jurors’ decisions. She is ecstatic to be involved in the field of sociology as it moves from explaining current and past social events to a more future-focused searching for iterative social patterns through the use of large datasets and computer programming. Hannah is incredibly grateful for the support of her family and mentors.
Project: Textual Analysis of Supreme Court Oral Arguments
This project seeks to determine the correlation between the questions asked by Supreme Court justices during oral argument appeals and justices’ votes to affirm or reverse the decision. Using oral argument transcripts from the past 28 years, this project will primarily utilize Mathematica’s machine learning to examine these transcripts, attempting to find a mechanism that suggests which way a justice will vote according to the questions asked by said justice in trial. Secondly, this project will examine larger patterns of vote direction in the Supreme Court and suggest further areas of research with this data.
Main Results in Detail
- 1. Word usage is predictive of the votes cast in a case.
- 2. A characteristic sentiment profile of each justice’s speeches was created.
- 1. Further examine the link between emotionally classified language and votes.
- 2. Examine interactions between justices and attorneys.