# Legal and Political Stance Detection of SCOTUS Language

arXiv: https://arxiv.org/abs/2211.11724 · HTML: https://mlsanity.com/paper/2211.11724/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 57 references that are in the map.
- First posted on arXiv 21 Nov 2022.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer](https://mlsanity.com/paper/2005.00052/) (2020)
- [AdapterHub: A Framework for Adapting Transformers](https://mlsanity.com/paper/2007.07779/) (2020)
- [LexGLUE: A Benchmark Dataset for Legal Language Understanding in English](https://mlsanity.com/paper/2110.00976/) (2021)
- [CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review](https://mlsanity.com/paper/2103.06268/) (2021)
- [When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset](https://mlsanity.com/paper/2104.08671/) (2021)
- [How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence](https://mlsanity.com/paper/2004.12158/) (2020)
- [Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations](https://mlsanity.com/paper/2010.03640/) (2020)
- [A Statutory Article Retrieval Dataset in French](https://mlsanity.com/paper/2108.11792/) (2021)
- [Orthogonal Language and Task Adapters in Zero-Shot Cross-Lingual Transfer](https://mlsanity.com/paper/2012.06460/) (2020)
- [Stance detection in online discussions](https://mlsanity.com/paper/1701.00504/) (2017)

## Built on by (most cited first, 5 of 5)

- [LaCour!: Enabling Research on Argumentation in Hearings of the European Court of Human Rights](https://mlsanity.com/paper/2312.05061/) (2023)
- [The Perfect Victim: Computational Analysis of Judicial Attitudes towards Victims of Sexual Violence](https://mlsanity.com/paper/2305.05302/) (2023)
- [Voting Booklet Bias: Stance Detection in Swiss Federal Communication](https://mlsanity.com/paper/2306.08999/) (2023)
- [Confidence-Calibrated Ensemble Dense Phrase Retrieval](https://mlsanity.com/paper/2306.15917/) (2023)
- [Stop Removing Stopwords: How an Inherited Preprocessing Default Distorts Legal Text-as-Data](https://mlsanity.com/paper/2609.19153/) (2026)

## Abstract (arXiv)

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.