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.

翻译：我们用自动姿态检测分析公开提供的美国最高法院文件。 在工作的第一阶段,我们调查法院公用语言的政治性程度。我们提出并计算SCOTUS法官使用口头辩论笔录的两种不同的意识形态指标。然后将这些基于语言的衡量标准与最高法院和公众现有的社会科学衡量标准进行比较。通过这一跨学科分析,我们发现对公众意见更敏感的法官往往在口头辩论中表达他们的意识形态。这一观察为最高法院司法行为的态度变化假设提供了一种新的证据。作为这种政治立场探测的自然延伸,我们建议用我们新的数据集SC系统来进行更专门的法律立场探测任务,该数据集与法律问题的书面意见相匹配。我们利用受过法律文件培训的语言调整者来发现这一数据集的竞争性表现。