<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="rowlands-etal-2024-predicting">
    <titleInfo>
        <title>Predicting Narratives of Climate Obstruction in Social Media Advertising</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Harri</namePart>
        <namePart type="family">Rowlands</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Gaku</namePart>
        <namePart type="family">Morio</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Dylan</namePart>
        <namePart type="family">Tanner</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Christopher</namePart>
        <namePart type="family">Manning</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: ACL 2024</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Lun-Wei</namePart>
            <namePart type="family">Ku</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Andre</namePart>
            <namePart type="family">Martins</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Vivek</namePart>
            <namePart type="family">Srikumar</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Bangkok, Thailand</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Social media advertising offers a platform for fossil fuel value chain companies and their agents to reinforce their narratives, often emphasizing economic, labor market, and energy security benefits to promote oil and gas policy and products. Whether such narratives can be detected automatically and the extent to which the cost of human annotation can be reduced is our research question. We introduce a task of classifying narratives into seven categories, based on existing definitions and data.Experiments showed that RoBERTa-large outperforms other methods, while GPT-4 Turbo can serve as a viable annotator for the task, thereby reducing human annotation costs. Our findings and insights provide guidance to automate climate-related ad analysis and lead to more scalable ad scrutiny.</abstract>
    <identifier type="citekey">rowlands-etal-2024-predicting</identifier>
    <identifier type="doi">10.18653/v1/2024.findings-acl.330</identifier>
    <location>
        <url>https://aclanthology.org/2024.findings-acl.330</url>
    </location>
    <part>
        <date>2024-08</date>
        <extent unit="page">
            <start>5547</start>
            <end>5558</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Predicting Narratives of Climate Obstruction in Social Media Advertising
%A Rowlands, Harri
%A Morio, Gaku
%A Tanner, Dylan
%A Manning, Christopher
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics: ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F rowlands-etal-2024-predicting
%X Social media advertising offers a platform for fossil fuel value chain companies and their agents to reinforce their narratives, often emphasizing economic, labor market, and energy security benefits to promote oil and gas policy and products. Whether such narratives can be detected automatically and the extent to which the cost of human annotation can be reduced is our research question. We introduce a task of classifying narratives into seven categories, based on existing definitions and data.Experiments showed that RoBERTa-large outperforms other methods, while GPT-4 Turbo can serve as a viable annotator for the task, thereby reducing human annotation costs. Our findings and insights provide guidance to automate climate-related ad analysis and lead to more scalable ad scrutiny.
%R 10.18653/v1/2024.findings-acl.330
%U https://aclanthology.org/2024.findings-acl.330
%U https://doi.org/10.18653/v1/2024.findings-acl.330
%P 5547-5558

Markdown (Informal)

[Predicting Narratives of Climate Obstruction in Social Media Advertising](https://aclanthology.org/2024.findings-acl.330) (Rowlands et al., Findings 2024)

• Predicting Narratives of Climate Obstruction in Social Media Advertising (Rowlands et al., Findings 2024)

ACL

• Harri Rowlands, Gaku Morio, Dylan Tanner, and Christopher Manning. 2024. Predicting Narratives of Climate Obstruction in Social Media Advertising. In Findings of the Association for Computational Linguistics: ACL 2024, pages 5547–5558, Bangkok, Thailand. Association for Computational Linguistics.