<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="fang-etal-2023-votes">
    <titleInfo>
        <title>More than Votes? Voting and Language based Partisanship in the US Supreme Court</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Biaoyan</namePart>
        <namePart type="family">Fang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Trevor</namePart>
        <namePart type="family">Cohn</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Timothy</namePart>
        <namePart type="family">Baldwin</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Lea</namePart>
        <namePart type="family">Frermann</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: EMNLP 2023</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Houda</namePart>
            <namePart type="family">Bouamor</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Juan</namePart>
            <namePart type="family">Pino</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kalika</namePart>
            <namePart type="family">Bali</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Singapore</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Understanding the prevalence and dynamics of justice partisanship and ideology in the US Supreme Court is critical in studying jurisdiction. Most research quantifies partisanship based on voting behavior, and oral arguments in the courtroom — the last essential procedure before the final case outcome — have not been well studied for this purpose. To address this gap, we present a framework for analyzing the language of justices in the courtroom for partisan signals, and study how partisanship in speech aligns with voting patterns. Our results show that the affiliated party of justices can be predicted reliably from their oral contributions. We further show a strong correlation between language partisanship and voting ideology.</abstract>
    <identifier type="citekey">fang-etal-2023-votes</identifier>
    <identifier type="doi">10.18653/v1/2023.findings-emnlp.306</identifier>
    <location>
        <url>https://aclanthology.org/2023.findings-emnlp.306</url>
    </location>
    <part>
        <date>2023-12</date>
        <extent unit="page">
            <start>4604</start>
            <end>4614</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T More than Votes? Voting and Language based Partisanship in the US Supreme Court
%A Fang, Biaoyan
%A Cohn, Trevor
%A Baldwin, Timothy
%A Frermann, Lea
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F fang-etal-2023-votes
%X Understanding the prevalence and dynamics of justice partisanship and ideology in the US Supreme Court is critical in studying jurisdiction. Most research quantifies partisanship based on voting behavior, and oral arguments in the courtroom — the last essential procedure before the final case outcome — have not been well studied for this purpose. To address this gap, we present a framework for analyzing the language of justices in the courtroom for partisan signals, and study how partisanship in speech aligns with voting patterns. Our results show that the affiliated party of justices can be predicted reliably from their oral contributions. We further show a strong correlation between language partisanship and voting ideology.
%R 10.18653/v1/2023.findings-emnlp.306
%U https://aclanthology.org/2023.findings-emnlp.306
%U https://doi.org/10.18653/v1/2023.findings-emnlp.306
%P 4604-4614

Markdown (Informal)

[More than Votes? Voting and Language based Partisanship in the US Supreme Court](https://aclanthology.org/2023.findings-emnlp.306) (Fang et al., Findings 2023)

• More than Votes? Voting and Language based Partisanship in the US Supreme Court (Fang et al., Findings 2023)

ACL

• Biaoyan Fang, Trevor Cohn, Timothy Baldwin, and Lea Frermann. 2023. More than Votes? Voting and Language based Partisanship in the US Supreme Court. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 4604–4614, Singapore. Association for Computational Linguistics.