Legal and Political Stance Detection of SCOTUS Language
This work addresses the problem of understanding judicial behavior and stance detection for legal and political science researchers, but it is incremental as it builds on existing methods and datasets.
The study tackled the problem of detecting political and legal stances in US Supreme Court language by proposing ideology metrics from oral argument transcripts and comparing them to existing measures, finding that justices more responsive to public opinion express their ideology during arguments, with competitive performance on a new legal stance detection dataset using legal document-trained adapters.
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.