Towards Reproducible Research of Event Detection Techniques for Twitter

Lade...
Vorschaubild
Dateien
Zu diesem Dokument gibt es keine Dateien.
Datum
2019
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
URI (zitierfähiger Link)
ArXiv-ID
Internationale Patentnummer
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Core Facility der Universität Konstanz
Gesperrt bis
Titel in einer weiteren Sprache
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published
Erschienen in
GEIGER, Melanie, ed.. 6th Swiss Conference on Data Science (SDS 2019), Bern, Switzerland, 14 June 2019. Piscataway, NJ: IEEE, 2019, pp. 69-74. ISBN 978-1-72813-105-4. Available under: doi: 10.1109/SDS.2019.000-5
Zusammenfassung

A major challenge in many research areas is reproducibility of implementations, experiments, or evaluations. New data sources and research directions complicate the reproducibility even more. For example, Twitter continues to gain popularity as a source of up-to-date news and information. As a result, numerous event detection techniques have been proposed to cope with the steadily increasing rate and volume of social media data streams. Although some of these works provide their implementation or conduct an evaluation of the proposed technique, it is almost impossible to reproduce their experiments. The main drawback is that Twitter prohibits the release of crawled datasets that are used by researchers in their experiments. In this work, we present a survey of the vast landscape of implementations, experiments, and evaluations presented by the different research works. Furthermore, we propose a reproducibility toolkit including Twistor (Twitter Stream Simulator), which can be used to simulate an artificial Twitter data stream (including events) as input for the experiments or evaluations of event detection techniques. We further present the experimental application of the reproducibility toolkit to state-of-the-art event detection techniques.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
Konferenz
6th Swiss Conference on Data Science (SDS 2019), 14. Juni 2019, Bern, Switzerland
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690WEILER, Andreas, Harry SCHILLING, Lukas KIRCHER, Michael GROSSNIKLAUS, 2019. Towards Reproducible Research of Event Detection Techniques for Twitter. 6th Swiss Conference on Data Science (SDS 2019). Bern, Switzerland, 14. Juni 2019. In: GEIGER, Melanie, ed.. 6th Swiss Conference on Data Science (SDS 2019), Bern, Switzerland, 14 June 2019. Piscataway, NJ: IEEE, 2019, pp. 69-74. ISBN 978-1-72813-105-4. Available under: doi: 10.1109/SDS.2019.000-5
BibTex
@inproceedings{Weiler2019-06Towar-46766,
  year={2019},
  doi={10.1109/SDS.2019.000-5},
  title={Towards Reproducible Research of Event Detection Techniques for Twitter},
  isbn={978-1-72813-105-4},
  publisher={IEEE},
  address={Piscataway, NJ},
  booktitle={6th Swiss Conference on Data Science (SDS 2019), Bern, Switzerland, 14 June 2019},
  pages={69--74},
  editor={Geiger, Melanie},
  author={Weiler, Andreas and Schilling, Harry and Kircher, Lukas and Grossniklaus, Michael}
}
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/123456789/46766">
    <dc:creator>Weiler, Andreas</dc:creator>
    <dc:contributor>Schilling, Harry</dc:contributor>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/46766"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:language>eng</dc:language>
    <dc:contributor>Weiler, Andreas</dc:contributor>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2019-09-02T12:30:57Z</dc:date>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:issued>2019-06</dcterms:issued>
    <dc:contributor>Kircher, Lukas</dc:contributor>
    <dc:creator>Kircher, Lukas</dc:creator>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:title>Towards Reproducible Research of Event Detection Techniques for Twitter</dcterms:title>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:contributor>Grossniklaus, Michael</dc:contributor>
    <dcterms:abstract xml:lang="eng">A major challenge in many research areas is reproducibility of implementations, experiments, or evaluations. New data sources and research directions complicate the reproducibility even more. For example, Twitter continues to gain popularity as a source of up-to-date news and information. As a result, numerous event detection techniques have been proposed to cope with the steadily increasing rate and volume of social media data streams. Although some of these works provide their implementation or conduct an evaluation of the proposed technique, it is almost impossible to reproduce their experiments. The main drawback is that Twitter prohibits the release of crawled datasets that are used by researchers in their experiments. In this work, we present a survey of the vast landscape of implementations, experiments, and evaluations presented by the different research works. Furthermore, we propose a reproducibility toolkit including Twistor (Twitter Stream Simulator), which can be used to simulate an artificial Twitter data stream (including events) as input for the experiments or evaluations of event detection techniques. We further present the experimental application of the reproducibility toolkit to state-of-the-art event detection techniques.</dcterms:abstract>
    <dc:creator>Grossniklaus, Michael</dc:creator>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2019-09-02T12:30:57Z</dcterms:available>
    <dc:creator>Schilling, Harry</dc:creator>
  </rdf:Description>
</rdf:RDF>
Interner Vermerk
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Kontakt
URL der Originalveröffentl.
Prüfdatum der URL
Prüfungsdatum der Dissertation
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Diese Publikation teilen