A Computational Analysis of Oral Argument in the Supreme Court
Gregory M. Dickinson
As the most public component of the Supreme Court's decision-making process,
oral argument receives an out-sized share of attention in the popular media.
Despite its prominence, however, the basic function and operation of oral
argument as an institution remains poorly understood, as political scientists
and legal scholars continue to debate even the most fundamental questions about
its role.
  Past study of oral argument has tended to focus on discrete, quantifiable
attributes of oral argument, such as the number of questions asked to each
advocate, the party of the Justices' appointing president, or the ideological
implications of the case on appeal. Such studies allow broad generalizations
about oral argument and judicial decision making: Justices tend to vote in
accordance with their ideological preferences, and they tend to ask more
questions when they are skeptical of a party's position. But they tell us
little about the actual goings on at oral argument -- the running dialog
between Justice and advocate that is the heart of the institution.
  This Article fills that void, using machine learning techniques to, for the
first time, construct predictive models of judicial decision making based not
on oral argument's superficial features or on factors external to oral
argument, such as where the case falls on a liberal-conservative spectrum, but
on the actual content of the oral argument itself -- the Justices' questions to
each side. The resultant models offer an important new window into aspects of
oral argument that have long resisted empirical study, including the Justices'
individual questioning styles, how each expresses skepticism, and which of the
Justices' questions are most central to oral argument dialog.