Legal and Political Stance Detection of SCOTUS Language

Noah Bergam, Emily Allaway, Kathleen McKeown

Abstract:

We analyze publicly available US Supreme
Court documents using automated stance detection. In the first phase of our work, we
investigate the extent to which the Court’s
public-facing language is political. We propose and calculate two distinct ideology metrics of SCOTUS justices using oral argument
transcripts. We then compare these language based metrics to existing social scientific measures of the ideology of the Supreme Court
and the public. Through this cross-disciplinary
analysis, we find that justices who are more
responsive to public opinion tend to express
their ideology during oral arguments. This
observation provides a new kind of evidence
in favor of the attitudinal change hypothesis
of Supreme Court justice behavior. As a natural extension of this political stance detection, we propose the more specialized task of
legal stance detection with our new dataset
SC-stance, which matches written opinions
to legal questions. We find competitive performance on this dataset using language adapters
trained on legal documents.