<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="bonaldi-etal-2024-nlp">
    <titleInfo>
        <title>NLP for Counterspeech against Hate: A Survey and How-To Guide</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Helena</namePart>
        <namePart type="family">Bonaldi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yi-Ling</namePart>
        <namePart type="family">Chung</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Gavin</namePart>
        <namePart type="family">Abercrombie</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Marco</namePart>
        <namePart type="family">Guerini</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>In recent years, counterspeech has emerged as one of the most promising strategies to fight online hate. These non-escalatory responses tackle online abuse while preserving the freedom of speech of the users, and can have a tangible impact in reducing online and offline violence. Recently, there has been growing interest from the Natural Language Processing (NLP) community in addressing the challenges of analysing, collecting, classifying, and automatically generating counterspeech, to reduce the huge burden of manually producing it. In particular, researchers have taken different directions in addressing these challenges, thus providing a variety of related tasks and resources. In this paper, we provide a guide for doing research on counterspeech, by describing - with detailed examples - the steps to undertake, and providing best practices that can be learnt from the NLP studies on this topic. Finally, we discuss open challenges and future directions of counterspeech research in NLP.</abstract>
    <identifier type="citekey">bonaldi-etal-2024-nlp</identifier>
    <identifier type="doi">10.18653/v1/2024.findings-naacl.221</identifier>
    <location>
        <url>https://aclanthology.org/2024.findings-naacl.221</url>
    </location>
    <part>
        <date>2024-06</date>
        <extent unit="page">
            <start>3480</start>
            <end>3499</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T NLP for Counterspeech against Hate: A Survey and How-To Guide
%A Bonaldi, Helena
%A Chung, Yi-Ling
%A Abercrombie, Gavin
%A Guerini, Marco
%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 bonaldi-etal-2024-nlp
%X In recent years, counterspeech has emerged as one of the most promising strategies to fight online hate. These non-escalatory responses tackle online abuse while preserving the freedom of speech of the users, and can have a tangible impact in reducing online and offline violence. Recently, there has been growing interest from the Natural Language Processing (NLP) community in addressing the challenges of analysing, collecting, classifying, and automatically generating counterspeech, to reduce the huge burden of manually producing it. In particular, researchers have taken different directions in addressing these challenges, thus providing a variety of related tasks and resources. In this paper, we provide a guide for doing research on counterspeech, by describing - with detailed examples - the steps to undertake, and providing best practices that can be learnt from the NLP studies on this topic. Finally, we discuss open challenges and future directions of counterspeech research in NLP.
%R 10.18653/v1/2024.findings-naacl.221
%U https://aclanthology.org/2024.findings-naacl.221
%U https://doi.org/10.18653/v1/2024.findings-naacl.221
%P 3480-3499

Markdown (Informal)

[NLP for Counterspeech against Hate: A Survey and How-To Guide](https://aclanthology.org/2024.findings-naacl.221) (Bonaldi et al., Findings 2024)

• NLP for Counterspeech against Hate: A Survey and How-To Guide (Bonaldi et al., Findings 2024)

ACL

• Helena Bonaldi, Yi-Ling Chung, Gavin Abercrombie, and Marco Guerini. 2024. NLP for Counterspeech against Hate: A Survey and How-To Guide. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 3480–3499, Mexico City, Mexico. Association for Computational Linguistics.