Survey and Experimental Analysis of Event Detection Techniques for Twitter

Loading...
Thumbnail Image
Date
2017
Editors
Contact
Journal ISSN
Electronic ISSN
ISBN
Bibliographical data
Publisher
Series
URI (citable link)
DOI (citable link)
ArXiv-ID
International patent number
Link to the license
EU project number
Project
Open Access publication
Restricted until
Title in another language
Research Projects
Organizational Units
Journal Issue
Publication type
Journal article
Publication status
Published
Published in
The Computer Journal ; 60 (2017), 3. - pp. 329-346. - ISSN 0010-4620. - eISSN 1460-2067
Abstract
Twitter's popularity as a source of up-to-date news and information is constantly increasing. In response to this trend, numerous event detection techniques have been proposed to cope with the rate and volume of Twitter data streams. Although most of these works conduct some evaluation of the proposed technique, a comparative study is often omitted. In this paper, we present a survey and experimental analysis of state-of-the-art event detection techniques for Twitter data streams. In order to conduct this study, we define a series of measures to support the quantitative and qualitative comparison. We demonstrate the effectiveness of these measures by applying them to event detection techniques as well as to baseline approaches using real-world Twitter streaming data.
Summary in another language
Subject (DDC)
004 Computer Science
Keywords
performance evaluation, event detection, Twitter data streams
Conference
Review
undefined / . - undefined, undefined. - (undefined; undefined)
Cite This
ISO 690WEILER, Andreas, Michael GROSSNIKLAUS, Marc H. SCHOLL, 2017. Survey and Experimental Analysis of Event Detection Techniques for Twitter. In: The Computer Journal. 60(3), pp. 329-346. ISSN 0010-4620. eISSN 1460-2067. Available under: doi: 10.1093/comjnl/bxw056
BibTex
@article{Weiler2017Surve-37473,
  year={2017},
  doi={10.1093/comjnl/bxw056},
  title={Survey and Experimental Analysis of Event Detection Techniques for Twitter},
  number={3},
  volume={60},
  issn={0010-4620},
  journal={The Computer Journal},
  pages={329--346},
  author={Weiler, Andreas and Grossniklaus, Michael and Scholl, Marc H.}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/37473">
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:abstract xml:lang="eng">Twitter's popularity as a source of up-to-date news and information is constantly increasing. In response to this trend, numerous event detection techniques have been proposed to cope with the rate and volume of Twitter data streams. Although most of these works conduct some evaluation of the proposed technique, a comparative study is often omitted. In this paper, we present a survey and experimental analysis of state-of-the-art event detection techniques for Twitter data streams. In order to conduct this study, we define a series of measures to support the quantitative and qualitative comparison. We demonstrate the effectiveness of these measures by applying them to event detection techniques as well as to baseline approaches using real-world Twitter streaming data.</dcterms:abstract>
    <dc:contributor>Scholl, Marc H.</dc:contributor>
    <dc:contributor>Grossniklaus, Michael</dc:contributor>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2017-02-16T08:13:47Z</dcterms:available>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:rights>terms-of-use</dc:rights>
    <dc:creator>Grossniklaus, Michael</dc:creator>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:title>Survey and Experimental Analysis of Event Detection Techniques for Twitter</dcterms:title>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/37473/1/Weiler_0-370382.pdf"/>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/37473"/>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:contributor>Weiler, Andreas</dc:contributor>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/37473/1/Weiler_0-370382.pdf"/>
    <dc:creator>Scholl, Marc H.</dc:creator>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2017-02-16T08:13:47Z</dc:date>
    <dc:language>eng</dc:language>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dc:creator>Weiler, Andreas</dc:creator>
    <dcterms:issued>2017</dcterms:issued>
  </rdf:Description>
</rdf:RDF>
Internal note
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Contact
URL of original publication
Test date of URL
Examination date of dissertation
Method of financing
Comment on publication
Alliance license
Corresponding Authors der Uni Konstanz vorhanden
International Co-Authors
Bibliography of Konstanz
Yes
Refereed