<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="akter-anastasopoulos-2024-study">
    <titleInfo>
        <title>A Study on Scaling Up Multilingual News Framing Analysis</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Syeda</namePart>
        <namePart type="given">Sabrina</namePart>
        <namePart type="family">Akter</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Antonios</namePart>
        <namePart type="family">Anastasopoulos</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: NAACL 2024</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Kevin</namePart>
            <namePart type="family">Duh</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Helena</namePart>
            <namePart type="family">Gomez</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Steven</namePart>
            <namePart type="family">Bethard</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Mexico City, Mexico</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Media framing is the study of strategically selecting and presenting specific aspects of political issues to shape public opinion. Despite its relevance to almost all societies around the world, research has been limited due to the lack of available datasets and other resources. This study explores the possibility of dataset creation through crowdsourcing, utilizing non-expert annotators to develop training corpora. We first extend framing analysis beyond English news to a multilingual context (12 typologically diverse languages) through automatic translation. We also present a novel benchmark in Bengali and Portuguese on the immigration and same-sex marriage domains.Additionally, we show that a system trained on our crowd-sourced dataset, combined with other existing ones, leads to a 5.32 percentage point increase from the baseline, showing that crowdsourcing is a viable option. Last, we study the performance of large language models (LLMs) for this task, finding that task-specific fine-tuning is a better approach than employing bigger non-specialized models.</abstract>
    <identifier type="citekey">akter-anastasopoulos-2024-study</identifier>
    <identifier type="doi">10.18653/v1/2024.findings-naacl.260</identifier>
    <location>
        <url>https://aclanthology.org/2024.findings-naacl.260</url>
    </location>
    <part>
        <date>2024-06</date>
        <extent unit="page">
            <start>4156</start>
            <end>4173</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T A Study on Scaling Up Multilingual News Framing Analysis
%A Akter, Syeda Sabrina
%A Anastasopoulos, Antonios
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F akter-anastasopoulos-2024-study
%X Media framing is the study of strategically selecting and presenting specific aspects of political issues to shape public opinion. Despite its relevance to almost all societies around the world, research has been limited due to the lack of available datasets and other resources. This study explores the possibility of dataset creation through crowdsourcing, utilizing non-expert annotators to develop training corpora. We first extend framing analysis beyond English news to a multilingual context (12 typologically diverse languages) through automatic translation. We also present a novel benchmark in Bengali and Portuguese on the immigration and same-sex marriage domains.Additionally, we show that a system trained on our crowd-sourced dataset, combined with other existing ones, leads to a 5.32 percentage point increase from the baseline, showing that crowdsourcing is a viable option. Last, we study the performance of large language models (LLMs) for this task, finding that task-specific fine-tuning is a better approach than employing bigger non-specialized models.
%R 10.18653/v1/2024.findings-naacl.260
%U https://aclanthology.org/2024.findings-naacl.260
%U https://doi.org/10.18653/v1/2024.findings-naacl.260
%P 4156-4173

Markdown (Informal)

[A Study on Scaling Up Multilingual News Framing Analysis](https://aclanthology.org/2024.findings-naacl.260) (Akter & Anastasopoulos, Findings 2024)

• A Study on Scaling Up Multilingual News Framing Analysis (Akter & Anastasopoulos, Findings 2024)

ACL

• Syeda Sabrina Akter and Antonios Anastasopoulos. 2024. A Study on Scaling Up Multilingual News Framing Analysis. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 4156–4173, Mexico City, Mexico. Association for Computational Linguistics.