<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wang-goutte-2017-detecting">
    <titleInfo>
        <title>Detecting Changes in Twitter Streams using Temporal Clusters of Hashtags</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Yunli</namePart>
        <namePart type="family">Wang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Cyril</namePart>
        <namePart type="family">Goutte</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 Events and Stories in the News Workshop</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Tommaso</namePart>
            <namePart type="family">Caselli</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ben</namePart>
            <namePart type="family">Miller</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Marieke</namePart>
            <namePart type="family">van Erp</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Piek</namePart>
            <namePart type="family">Vossen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Martha</namePart>
            <namePart type="family">Palmer</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Eduard</namePart>
            <namePart type="family">Hovy</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Teruko</namePart>
            <namePart type="family">Mitamura</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Caswell</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>Detecting events from social media data has important applications in public security, political issues, and public health. Many studies have focused on detecting specific or unspecific events from Twitter streams. However, not much attention has been paid to detecting changes, and their impact, in online conversations related to an event. We propose methods for detecting such changes, using clustering of temporal profiles of hashtags, and three change point detection algorithms. The methods were tested on two Twitter datasets: one covering the 2014 Ottawa shooting event, and one covering the Sochi winter Olympics. We compare our approach to a baseline consisting of detecting change from raw counts in the conversation. We show that our method produces large gains in change detection accuracy on both datasets.</abstract>
    <identifier type="citekey">wang-goutte-2017-detecting</identifier>
    <identifier type="doi">10.18653/v1/W17-2702</identifier>
    <location>
        <url>https://aclanthology.org/W17-2702</url>
    </location>
    <part>
        <date>2017-08</date>
        <extent unit="page">
            <start>10</start>
            <end>14</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Detecting Changes in Twitter Streams using Temporal Clusters of Hashtags
%A Wang, Yunli
%A Goutte, Cyril
%Y Caselli, Tommaso
%Y Miller, Ben
%Y van Erp, Marieke
%Y Vossen, Piek
%Y Palmer, Martha
%Y Hovy, Eduard
%Y Mitamura, Teruko
%Y Caswell, David
%S Proceedings of the Events and Stories in the News Workshop
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F wang-goutte-2017-detecting
%X Detecting events from social media data has important applications in public security, political issues, and public health. Many studies have focused on detecting specific or unspecific events from Twitter streams. However, not much attention has been paid to detecting changes, and their impact, in online conversations related to an event. We propose methods for detecting such changes, using clustering of temporal profiles of hashtags, and three change point detection algorithms. The methods were tested on two Twitter datasets: one covering the 2014 Ottawa shooting event, and one covering the Sochi winter Olympics. We compare our approach to a baseline consisting of detecting change from raw counts in the conversation. We show that our method produces large gains in change detection accuracy on both datasets.
%R 10.18653/v1/W17-2702
%U https://aclanthology.org/W17-2702
%U https://doi.org/10.18653/v1/W17-2702
%P 10-14

Markdown (Informal)

[Detecting Changes in Twitter Streams using Temporal Clusters of Hashtags](https://aclanthology.org/W17-2702) (Wang & Goutte, EventStory 2017)

• Detecting Changes in Twitter Streams using Temporal Clusters of Hashtags (Wang & Goutte, EventStory 2017)

ACL

• Yunli Wang and Cyril Goutte. 2017. Detecting Changes in Twitter Streams using Temporal Clusters of Hashtags. In Proceedings of the Events and Stories in the News Workshop, pages 10–14, Vancouver, Canada. Association for Computational Linguistics.