Story Tracker : incremental visual text analytics of news story development

Lade...
Vorschaubild
Dateien
Zu diesem Dokument gibt es keine Dateien.
Datum
2013
Autor:innen
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
ArXiv-ID
Internationale Patentnummer
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Open Access Green
Core Facility der Universität Konstanz
Gesperrt bis
Titel in einer weiteren Sprache
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Publikationstyp
Zeitschriftenartikel
Publikationsstatus
Published
Erschienen in
Information Visualization. 2013, 12(3-4), pp. 308-323. ISSN 1473-8716. eISSN 1473-8724. Available under: doi: 10.1177/1473871613493996
Zusammenfassung

Online news sources produce thousands of news articles every day, reporting on local and global real-world events. New information quickly replaces the old, making it difficult for readers to put current events in the context of the past. The stories about these events have complex relationships and characteristics that are difficult to model: they can be weakly or strongly related or they can merge or split over time. In this article, we present a visual analytics system for temporal analysis of news stories in dynamic information streams, which combines interactive visualization and text mining techniques to facilitate the analysis of similar topics that split and merge over time. Text clustering algorithms extract stories from online news streams in consecutive time windows and identify similar stories from the past. The stories are displayed in a visualization, which (1) sorts the stories by minimizing clutter and overlap from edge crossings, (2) shows their temporal characteristics in different time frames with different levels of detail, and (3) allows incremental updates of the display without recalculating the past data. Stories can be interactively filtered by their duration and connectivity in order to be explored in full detail. To demonstrate the system’s capabilities for detailed dynamic text stream exploration, we present a use case with real news data about the Arabic Uprising in 2011.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
News stream analysis, topic evolution, dynamic visualization, text analytics
Konferenz
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690KRSTAJIC, Milos, Mohammad NAJM-ARAGHI, Florian MANSMANN, Daniel A. KEIM, 2013. Story Tracker : incremental visual text analytics of news story development. In: Information Visualization. 2013, 12(3-4), pp. 308-323. ISSN 1473-8716. eISSN 1473-8724. Available under: doi: 10.1177/1473871613493996
BibTex
@article{Krstajic2013Story-26224,
  year={2013},
  doi={10.1177/1473871613493996},
  title={Story Tracker : incremental visual text analytics of news story development},
  number={3-4},
  volume={12},
  issn={1473-8716},
  journal={Information Visualization},
  pages={308--323},
  author={Krstajic, Milos and Najm-Araghi, Mohammad and Mansmann, Florian and Keim, Daniel A.}
}
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/123456789/26224">
    <dcterms:abstract xml:lang="eng">Online news sources produce thousands of news articles every day, reporting on local and global real-world events. New information quickly replaces the old, making it difficult for readers to put current events in the context of the past. The stories about these events have complex relationships and characteristics that are difficult to model: they can be weakly or strongly related or they can merge or split over time. In this article, we present a visual analytics system for temporal analysis of news stories in dynamic information streams, which combines interactive visualization and text mining techniques to facilitate the analysis of similar topics that split and merge over time. Text clustering algorithms extract stories from online news streams in consecutive time windows and identify similar stories from the past. The stories are displayed in a visualization, which (1) sorts the stories by minimizing clutter and overlap from edge crossings, (2) shows their temporal characteristics in different time frames with different levels of detail, and (3) allows incremental updates of the display without recalculating the past data. Stories can be interactively filtered by their duration and connectivity in order to be explored in full detail. To demonstrate the system’s capabilities for detailed dynamic text stream exploration, we present a use case with real news data about the Arabic Uprising in 2011.</dcterms:abstract>
    <dc:contributor>Mansmann, Florian</dc:contributor>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/26224/2/Krstajic_262244.pdf"/>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:creator>Najm-Araghi, Mohammad</dc:creator>
    <dc:contributor>Najm-Araghi, Mohammad</dc:contributor>
    <dc:creator>Krstajic, Milos</dc:creator>
    <dc:creator>Mansmann, Florian</dc:creator>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:issued>2013</dcterms:issued>
    <dc:contributor>Krstajic, Milos</dc:contributor>
    <dcterms:title>Story Tracker : incremental visual text analytics of news story development</dcterms:title>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2014-02-08T06:53:57Z</dcterms:available>
    <dc:creator>Keim, Daniel A.</dc:creator>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2014-02-08T06:53:57Z</dc:date>
    <dcterms:bibliographicCitation>Information Visualization ; 12 (2013), 3-4. - S. 308-323</dcterms:bibliographicCitation>
    <dc:rights>terms-of-use</dc:rights>
    <dc:contributor>Keim, Daniel A.</dc:contributor>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/26224"/>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/26224/2/Krstajic_262244.pdf"/>
    <dc:language>eng</dc:language>
  </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