<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="bahuleyan-vechtomova-2017-uwaterloo">
    <titleInfo>
        <title>UWaterloo at SemEval-2017 Task 8: Detecting Stance towards Rumours with Topic Independent Features</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Hareesh</namePart>
        <namePart type="family">Bahuleyan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Olga</namePart>
        <namePart type="family">Vechtomova</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2017-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Steven</namePart>
            <namePart type="family">Bethard</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Marine</namePart>
            <namePart type="family">Carpuat</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Marianna</namePart>
            <namePart type="family">Apidianaki</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Saif</namePart>
            <namePart type="given">M</namePart>
            <namePart type="family">Mohammad</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Daniel</namePart>
            <namePart type="family">Cer</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Jurgens</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Vancouver, Canada</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>This paper describes our system for subtask-A: SDQC for RumourEval, task-8 of SemEval 2017. Identifying rumours, especially for breaking news events as they unfold, is a challenging task due to the absence of sufficient information about the exact rumour stories circulating on social media. Determining the stance of Twitter users towards rumourous messages could provide an indirect way of identifying potential rumours. The proposed approach makes use of topic independent features from two categories, namely cue features and message specific features to fit a gradient boosting classifier. With an accuracy of 0.78, our system achieved the second best performance on subtask-A of RumourEval.</abstract>
    <identifier type="citekey">bahuleyan-vechtomova-2017-uwaterloo</identifier>
    <identifier type="doi">10.18653/v1/S17-2080</identifier>
    <location>
        <url>https://aclanthology.org/S17-2080</url>
    </location>
    <part>
        <date>2017-08</date>
        <extent unit="page">
            <start>461</start>
            <end>464</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T UWaterloo at SemEval-2017 Task 8: Detecting Stance towards Rumours with Topic Independent Features
%A Bahuleyan, Hareesh
%A Vechtomova, Olga
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F bahuleyan-vechtomova-2017-uwaterloo
%X This paper describes our system for subtask-A: SDQC for RumourEval, task-8 of SemEval 2017. Identifying rumours, especially for breaking news events as they unfold, is a challenging task due to the absence of sufficient information about the exact rumour stories circulating on social media. Determining the stance of Twitter users towards rumourous messages could provide an indirect way of identifying potential rumours. The proposed approach makes use of topic independent features from two categories, namely cue features and message specific features to fit a gradient boosting classifier. With an accuracy of 0.78, our system achieved the second best performance on subtask-A of RumourEval.
%R 10.18653/v1/S17-2080
%U https://aclanthology.org/S17-2080
%U https://doi.org/10.18653/v1/S17-2080
%P 461-464

Markdown (Informal)

[UWaterloo at SemEval-2017 Task 8: Detecting Stance towards Rumours with Topic Independent Features](https://aclanthology.org/S17-2080) (Bahuleyan & Vechtomova, SemEval 2017)

• UWaterloo at SemEval-2017 Task 8: Detecting Stance towards Rumours with Topic Independent Features (Bahuleyan & Vechtomova, SemEval 2017)

ACL

• Hareesh Bahuleyan and Olga Vechtomova. 2017. UWaterloo at SemEval-2017 Task 8: Detecting Stance towards Rumours with Topic Independent Features. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 461–464, Vancouver, Canada. Association for Computational Linguistics.