Heard or Halted? Gender, Interruptions, and Emotional Tone in U.S. Supreme Court Oral Arguments
- URL: http://arxiv.org/abs/2512.05832v2
- Date: Thu, 11 Dec 2025 05:30:02 GMT
- Title: Heard or Halted? Gender, Interruptions, and Emotional Tone in U.S. Supreme Court Oral Arguments
- Abstract summary: We analyze 12,663 speech chunks from advocate-justice interactions to assess whether interruptions alter the meaning of an advocate's argument.<n>We find that semantic similarity between pre- and post-interruption speech remains consistently high.<n> interruptions directed at female advocates contain significantly higher levels of negative sentiment.
- Score: 2.28438857884398
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This study examines how interruptions during U.S. Supreme Court oral arguments shape both the semantic content and emotional tone of advocates' speech, with a focus on gendered dynamics in judicial discourse. Using the ConvoKit Supreme Court Corpus (2010-2019), we analyze 12,663 speech chunks from advocate-justice interactions to assess whether interruptions alter the meaning of an advocate's argument and whether interruptions toward female advocates exhibit more negative emotional valence. Semantic shifts are quantified using GloVe-based sentence embeddings, while sentiment is measured through lexicon-based analysis. We find that semantic similarity between pre- and post-interruption speech remains consistently high, suggesting that interruptions do not substantially alter argumentative content. However, interruptions directed at female advocates contain significantly higher levels of negative sentiment. These results deepen empirical understanding of gendered communication in elite institutional settings and demonstrate the value of computational linguistic methods for studying power, discourse, and equity in judicial proceedings.
Related papers
- The False Resonance: A Critical Examination of Emotion Embedding Similarity for Speech Generation Evaluation [66.28943752785906]
Objective metrics for emotional expressiveness are vital for speech generation, particularly in expressive synthesis and voice conversion.<n>We compute cosine similarity of embeddings from encoders like emotion2vec, assuming they capture affective cues despite linguistic and speaker variations.<n>Despite high classification accuracy, these latent spaces are unsuitable for zero-shot similarity evaluation.
arXiv Detail & Related papers (2026-04-29T06:59:48Z) - More Than Sum of Its Parts: Deciphering Intent Shifts in Multimodal Hate Speech Detection [49.07221734365168]
Hate speech on social media is critical for securing cyberspace, yet relies heavily on automated detection systems.<n>We characterize semantic intent shifts where modalities interact to construct implicit hate from benign cues or neutralize toxicity through semantic inversion.<n>We propose the Asymmetric Reasoning via Courtroom Agent DEbate framework to effectively decipher these complex cues.
arXiv Detail & Related papers (2026-03-22T15:46:23Z) - The Silent Thought: Modeling Internal Cognition in Full-Duplex Spoken Dialogue Models via Latent Reasoning [76.96796481909581]
Internal cognitive processing is instrumental in formulating high-quality responses.<n>We propose a novel method named FLAIR that conducts latent thinking simultaneously with speech perception.<n>Our approach aligns seamlessly with spoken dialogue systems.
arXiv Detail & Related papers (2026-03-18T15:30:29Z) - Latent Topic Synthesis: Leveraging LLMs for Electoral Ad Analysis [51.95395936342771]
We introduce an end-to-end framework for automatically generating an interpretable topic taxonomy from an unlabeled corpus.<n>We apply this framework to a large corpus of Meta political ads from the month ahead of the 2024 U.S. Presidential election.<n>Our approach uncovers latent discourse structures, synthesizes semantically rich topic labels, and annotates topics with moral framing dimensions.
arXiv Detail & Related papers (2025-10-16T20:30:20Z) - Beyond Binary Gender: Evaluating Gender-Inclusive Machine Translation with Ambiguous Attitude Words [85.48043537327258]
Existing machine translation gender bias evaluations are primarily focused on male and female genders.
This study presents a benchmark AmbGIMT (Gender-Inclusive Machine Translation with Ambiguous attitude words)
We propose a novel process to evaluate gender bias based on the Emotional Attitude Score (EAS), which is used to quantify ambiguous attitude words.
arXiv Detail & Related papers (2024-07-23T08:13:51Z) - Context Does Matter: Implications for Crowdsourced Evaluation Labels in Task-Oriented Dialogue Systems [57.16442740983528]
Crowdsourced labels play a crucial role in evaluating task-oriented dialogue systems.
Previous studies suggest using only a portion of the dialogue context in the annotation process.
This study investigates the influence of dialogue context on annotation quality.
arXiv Detail & Related papers (2024-04-15T17:56:39Z) - Hatred Stems from Ignorance! Distillation of the Persuasion Modes in Countering Conversational Hate Speech [0.0]
This study distills persuasion modes into reason, emotion, and credibility.<n>It evaluates their use in two types of conversation interactions: closed (multi-turn) and open (single-turn) concerning racism, sexism, and religious bigotry.
arXiv Detail & Related papers (2024-03-18T07:20:35Z) - A Computational Analysis of Oral Argument in the Supreme Court [0.0]
The basic function and operation of oral argument as an institution remains poorly understood.
This Article uses machine learning techniques to construct predictive models of judicial decision making.
It offers an important new window into aspects of oral argument that have long resisted empirical study.
arXiv Detail & Related papers (2023-06-05T13:52:56Z) - Textless Speech Emotion Conversion using Decomposed and Discrete
Representations [49.55101900501656]
We decompose speech into discrete and disentangled learned representations, consisting of content units, F0, speaker, and emotion.
First, we modify the speech content by translating the content units to a target emotion, and then predict the prosodic features based on these units.
Finally, the speech waveform is generated by feeding the predicted representations into a neural vocoder.
arXiv Detail & Related papers (2021-11-14T18:16:42Z) - Text as Causal Mediators: Research Design for Causal Estimates of
Differential Treatment of Social Groups via Language Aspects [7.175621752912443]
We propose a causal research design for observational (non-experimental) data to estimate the natural direct and indirect effects of social group signals on speakers' responses.
We illustrate the promises and challenges of this framework via a theoretical case study of the effect of an advocate's gender on interruptions from justices during U.S. Supreme Court oral arguments.
arXiv Detail & Related papers (2021-09-15T19:15:35Z) - How to Split: the Effect of Word Segmentation on Gender Bias in Speech
Translation [14.955696163410254]
We bring the analysis on gender bias in automatic translation onto a seemingly neutral yet critical component: word segmentation.
Our results on two language pairs (English-Italian/French) show that state-of-the-art sub-word splitting (BPE) comes at the cost of higher gender bias.
In light of this finding, we propose a combined approach that preserves BPE overall translation quality, while leveraging the higher ability of character-based segmentation to properly translate gender.
arXiv Detail & Related papers (2021-05-28T12:38:21Z) - Did they answer? Subjective acts and intents in conversational discourse [48.63528550837949]
We present the first discourse dataset with multiple and subjective interpretations of English conversation.
We show disagreements are nuanced and require a deeper understanding of the different contextual factors.
arXiv Detail & Related papers (2021-04-09T16:34:19Z) - Pardon the Interruption: An Analysis of Gender and Turn-Taking in U.S.
Supreme Court Oral Arguments [6.015556590955814]
This study presents a corpus of turn changes between speakers in U.S. Supreme Court oral arguments.
Each turn change is labeled on a spectrum of "cooperative" to "competitive" by a human annotator.
We analyze the relationship between speech features, the nature of exchanges, and the gender and legal role of the speakers.
arXiv Detail & Related papers (2020-09-15T23:37:48Z) - A Framework for the Computational Linguistic Analysis of Dehumanization [52.735780962665814]
We analyze discussions of LGBTQ people in the New York Times from 1986 to 2015.
We find increasingly humanizing descriptions of LGBTQ people over time.
The ability to analyze dehumanizing language at a large scale has implications for automatically detecting and understanding media bias as well as abusive language online.
arXiv Detail & Related papers (2020-03-06T03:02:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.