<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zeinert-etal-2021-annotating">
    <titleInfo>
        <title>Annotating Online Misogyny</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Philine</namePart>
        <namePart type="family">Zeinert</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Nanna</namePart>
        <namePart type="family">Inie</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Leon</namePart>
        <namePart type="family">Derczynski</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Chengqing</namePart>
            <namePart type="family">Zong</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Fei</namePart>
            <namePart type="family">Xia</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Wenjie</namePart>
            <namePart type="family">Li</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Roberto</namePart>
            <namePart type="family">Navigli</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Online misogyny, a category of online abusive language, has serious and harmful social consequences. Automatic detection of misogynistic language online, while imperative, poses complicated challenges to both data gathering, data annotation, and bias mitigation, as this type of data is linguistically complex and diverse. This paper makes three contributions in this area: Firstly, we describe the detailed design of our iterative annotation process and codebook. Secondly, we present a comprehensive taxonomy of labels for annotating misogyny in natural written language, and finally, we introduce a high-quality dataset of annotated posts sampled from social media posts.</abstract>
    <identifier type="citekey">zeinert-etal-2021-annotating</identifier>
    <identifier type="doi">10.18653/v1/2021.acl-long.247</identifier>
    <location>
        <url>https://aclanthology.org/2021.acl-long.247</url>
    </location>
    <part>
        <date>2021-08</date>
        <extent unit="page">
            <start>3181</start>
            <end>3197</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Annotating Online Misogyny
%A Zeinert, Philine
%A Inie, Nanna
%A Derczynski, Leon
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F zeinert-etal-2021-annotating
%X Online misogyny, a category of online abusive language, has serious and harmful social consequences. Automatic detection of misogynistic language online, while imperative, poses complicated challenges to both data gathering, data annotation, and bias mitigation, as this type of data is linguistically complex and diverse. This paper makes three contributions in this area: Firstly, we describe the detailed design of our iterative annotation process and codebook. Secondly, we present a comprehensive taxonomy of labels for annotating misogyny in natural written language, and finally, we introduce a high-quality dataset of annotated posts sampled from social media posts.
%R 10.18653/v1/2021.acl-long.247
%U https://aclanthology.org/2021.acl-long.247
%U https://doi.org/10.18653/v1/2021.acl-long.247
%P 3181-3197

Markdown (Informal)

[Annotating Online Misogyny](https://aclanthology.org/2021.acl-long.247) (Zeinert et al., ACL-IJCNLP 2021)

• Annotating Online Misogyny (Zeinert et al., ACL-IJCNLP 2021)

ACL

• Philine Zeinert, Nanna Inie, and Leon Derczynski. 2021. Annotating Online Misogyny. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 3181–3197, Online. Association for Computational Linguistics.