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.

本文使用自动态度检测技术分析了公开的美国最高法院文件，探讨了法院公开语言的政治性，并通过两个不同的意识形态指标比较了大法官的意识形态以及现有社会科学意识形态度量之间的差异。本文还在此基础上探讨了更具专业性的法律立场检测任务，并在法律文件上训练语言适配器，取得了较好的性能。

最高法院语言的法律和政治立场检测