COMPUTATIONAL ANALYSIS OF THE CONVERSATIONAL DYNAMICS OF THE UNITED STATES SUPREME COURT

Abstract

The decisions of the United States Supreme Court have far-reaching implications in American life. Using transcripts of Supreme Court oral arguments this work looks at the conversational dynamics of Supreme Court justices and links their conversational interaction with the decisions of the Court and individual justices. While several studies have looked at the relationship between oral arguments and case variables, to our knowledge, none have looked at the relationship between conversational dynamics and case outcomes. Working from this view, we show that the conversation of Supreme Court justices is both predictable and predictive. We aim to show that conversation during Supreme Court cases is patterned, this patterned conversation is associated with case outcomes, and that this association can be used to make predictions about case outcomes. We present three sets of experiments to accomplish this. The first examines the order of speakers during oral arguments as a patterned sequence, showing that cohesive elements in the discourse, along with references to individuals, provide significant improvements over our "bag-of-words" baseline in identifying speakers in sequence within a transcript. The second graphically examines the association between speaker <italic>turn-taking</italic> and case outcomes. The results presented with this experiment point to interesting and complex relationships between conversational interaction and case variables, such as justices' votes. The third experiment shows that this relationship can be used in the prediction of case outcomes with accuracy ranging from 62.5% to 76.8% for varying conditions. Finally, we offer recommendations for improved tools for legal researchers interested in the relationship between conversation during oral arguments and case outcomes, and suggestions for how these tools may be applied to more general problems.