<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="subramanian-etal-2018-content">
    <titleInfo>
        <title>Content-based Popularity Prediction of Online Petitions Using a Deep Regression Model</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Shivashankar</namePart>
        <namePart type="family">Subramanian</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Timothy</namePart>
        <namePart type="family">Baldwin</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Trevor</namePart>
        <namePart type="family">Cohn</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Melbourne, Australia</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Online petitions are a cost-effective way for citizens to collectively engage with policy-makers in a democracy. Predicting the popularity of a petition — commonly measured by its signature count — based on its textual content has utility for policymakers as well as those posting the petition. In this work, we model this task using CNN regression with an auxiliary ordinal regression objective. We demonstrate the effectiveness of our proposed approach using UK and US government petition datasets.</abstract>
    <identifier type="citekey">subramanian-etal-2018-content</identifier>
    <identifier type="doi">10.18653/v1/P18-2030</identifier>
    <location>
        <url>https://aclanthology.org/P18-2030</url>
    </location>
    <part>
        <date>2018-07</date>
        <extent unit="page">
            <start>182</start>
            <end>188</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Content-based Popularity Prediction of Online Petitions Using a Deep Regression Model
%A Subramanian, Shivashankar
%A Baldwin, Timothy
%A Cohn, Trevor
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F subramanian-etal-2018-content
%X Online petitions are a cost-effective way for citizens to collectively engage with policy-makers in a democracy. Predicting the popularity of a petition — commonly measured by its signature count — based on its textual content has utility for policymakers as well as those posting the petition. In this work, we model this task using CNN regression with an auxiliary ordinal regression objective. We demonstrate the effectiveness of our proposed approach using UK and US government petition datasets.
%R 10.18653/v1/P18-2030
%U https://aclanthology.org/P18-2030
%U https://doi.org/10.18653/v1/P18-2030
%P 182-188

Markdown (Informal)

[Content-based Popularity Prediction of Online Petitions Using a Deep Regression Model](https://aclanthology.org/P18-2030) (Subramanian et al., ACL 2018)

• Content-based Popularity Prediction of Online Petitions Using a Deep Regression Model (Subramanian et al., ACL 2018)

ACL

• Shivashankar Subramanian, Timothy Baldwin, and Trevor Cohn. 2018. Content-based Popularity Prediction of Online Petitions Using a Deep Regression Model. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 182–188, Melbourne, Australia. Association for Computational Linguistics.