<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wood-doughty-etal-2021-using">
    <titleInfo>
        <title>Using Noisy Self-Reports to Predict Twitter User Demographics</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Zach</namePart>
        <namePart type="family">Wood-Doughty</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Paiheng</namePart>
        <namePart type="family">Xu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Xiao</namePart>
        <namePart type="family">Liu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Mark</namePart>
        <namePart type="family">Dredze</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media</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">Cheng-Te</namePart>
            <namePart type="family">Li</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>Computational social science studies often contextualize content analysis within standard demographics. Since demographics are unavailable on many social media platforms (e.g. Twitter), numerous studies have inferred demographics automatically. Despite many studies presenting proof-of-concept inference of race and ethnicity, training of practical systems remains elusive since there are few annotated datasets. Existing datasets are small, inaccurate, or fail to cover the four most common racial and ethnic groups in the United States. We present a method to identify self-reports of race and ethnicity from Twitter profile descriptions. Despite the noise of automated supervision, our self-report datasets enable improvements in classification performance on gold standard self-report survey data. The result is a reproducible method for creating large-scale training resources for race and ethnicity.</abstract>
    <identifier type="citekey">wood-doughty-etal-2021-using</identifier>
    <identifier type="doi">10.18653/v1/2021.socialnlp-1.11</identifier>
    <location>
        <url>https://aclanthology.org/2021.socialnlp-1.11</url>
    </location>
    <part>
        <date>2021-06</date>
        <extent unit="page">
            <start>123</start>
            <end>137</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Using Noisy Self-Reports to Predict Twitter User Demographics
%A Wood-Doughty, Zach
%A Xu, Paiheng
%A Liu, Xiao
%A Dredze, Mark
%Y Ku, Lun-Wei
%Y Li, Cheng-Te
%S Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F wood-doughty-etal-2021-using
%X Computational social science studies often contextualize content analysis within standard demographics. Since demographics are unavailable on many social media platforms (e.g. Twitter), numerous studies have inferred demographics automatically. Despite many studies presenting proof-of-concept inference of race and ethnicity, training of practical systems remains elusive since there are few annotated datasets. Existing datasets are small, inaccurate, or fail to cover the four most common racial and ethnic groups in the United States. We present a method to identify self-reports of race and ethnicity from Twitter profile descriptions. Despite the noise of automated supervision, our self-report datasets enable improvements in classification performance on gold standard self-report survey data. The result is a reproducible method for creating large-scale training resources for race and ethnicity.
%R 10.18653/v1/2021.socialnlp-1.11
%U https://aclanthology.org/2021.socialnlp-1.11
%U https://doi.org/10.18653/v1/2021.socialnlp-1.11
%P 123-137

Markdown (Informal)

[Using Noisy Self-Reports to Predict Twitter User Demographics](https://aclanthology.org/2021.socialnlp-1.11) (Wood-Doughty et al., SocialNLP 2021)

• Using Noisy Self-Reports to Predict Twitter User Demographics (Wood-Doughty et al., SocialNLP 2021)

ACL

• Zach Wood-Doughty, Paiheng Xu, Xiao Liu, and Mark Dredze. 2021. Using Noisy Self-Reports to Predict Twitter User Demographics. In Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media, pages 123–137, Online. Association for Computational Linguistics.