Analytical Workbench for Integrated Social Media Geo-Inference

Lade...
Vorschaubild
Dateien
Zu diesem Dokument gibt es keine Dateien.
Datum
2018
Autor:innen
Mahtal, Sanae
Lupu, Cristina
Armbruster, Benedikt
Bechtold, Marvin
Reichel, Maximilian
Wangler, Thomas
Thom, Dennis
Koch, Steffen
Ertl, Thomas
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
DOI (zitierfähiger Link)
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
Beitrag zu einem Konferenzband
Publikationsstatus
Published
Erschienen in
BURGHARDT, Dirk, ed., Siming CHEN, ed., Gennady ANDRIENKO, ed., Natalia ANDRIENKO, ed., Ross PURVES, ed., Alexandra DIEHL, ed.. VGI Geovisual Analytics Workshop. 2018
Zusammenfassung

In the realm of social media monitoring and analysis, the availability of location-based information is of pivotal importance to understand the spatial behavior of social media users. Especially in fields like disaster management and urban planning, such data holds huge value for analysts and decision makers alike. However, as only few posts and messages in platforms like Twitter are already provided with GPS-coordinates or geo-tags by the users, researchers have proposed various algorithmic and modeldriven means to infer this information from properties like the content, network, or geographic history of the users. Since many of these methods only focus on isolated features or specific models, this paper presents a comprehensive framework that allows to integrate, combine and compare multiple geo-inference schemes in a unified, standardized, and performance-optimized fashion. In addition to that, we present a visual interface, which offers an intuitive, real-time assessment of the accuracy of singular and combined methods as well as support in detecting and understanding possible anomalies.We demonstrate the usefulness and relevance of our approach in a comprehensive case study.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
VGI, Geo-inference, Geo-prediction, Visual Analytics
Konferenz
VGI Geovisual Analytics Workshop, colocated with BDVA 2018, 19. Okt. 2018, Konstanz, Germany
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690MAHTAL, Sanae, Cristina LUPU, Benedikt ARMBRUSTER, Marvin BECHTOLD, Maximilian REICHEL, Thomas WANGLER, Dennis THOM, Steffen KOCH, Thomas ERTL, 2018. Analytical Workbench for Integrated Social Media Geo-Inference. VGI Geovisual Analytics Workshop, colocated with BDVA 2018. Konstanz, Germany, 19. Okt. 2018. In: BURGHARDT, Dirk, ed., Siming CHEN, ed., Gennady ANDRIENKO, ed., Natalia ANDRIENKO, ed., Ross PURVES, ed., Alexandra DIEHL, ed.. VGI Geovisual Analytics Workshop. 2018
BibTex
@inproceedings{Mahtal2018Analy-44251,
  year={2018},
  title={Analytical Workbench for Integrated Social Media Geo-Inference},
  booktitle={VGI Geovisual Analytics Workshop},
  editor={Burghardt, Dirk and Chen, Siming and Andrienko, Gennady and Andrienko, Natalia and Purves, Ross and Diehl, Alexandra},
  author={Mahtal, Sanae and Lupu, Cristina and Armbruster, Benedikt and Bechtold, Marvin and Reichel, Maximilian and Wangler, Thomas and Thom, Dennis and Koch, Steffen and Ertl, Thomas}
}
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/123456789/44251">
    <dc:creator>Reichel, Maximilian</dc:creator>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dcterms:title>Analytical Workbench for Integrated Social Media Geo-Inference</dcterms:title>
    <dc:language>eng</dc:language>
    <dc:contributor>Thom, Dennis</dc:contributor>
    <dc:contributor>Bechtold, Marvin</dc:contributor>
    <dc:contributor>Armbruster, Benedikt</dc:contributor>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/44251/1/Mahtal_2-rzgd1myf2nng1.pdf"/>
    <dc:creator>Wangler, Thomas</dc:creator>
    <dc:contributor>Koch, Steffen</dc:contributor>
    <dc:creator>Armbruster, Benedikt</dc:creator>
    <dc:rights>terms-of-use</dc:rights>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2018-12-10T15:09:29Z</dcterms:available>
    <dc:creator>Thom, Dennis</dc:creator>
    <dc:contributor>Ertl, Thomas</dc:contributor>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:issued>2018</dcterms:issued>
    <dc:contributor>Wangler, Thomas</dc:contributor>
    <dc:contributor>Reichel, Maximilian</dc:contributor>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2018-12-10T15:09:29Z</dc:date>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/44251"/>
    <dcterms:abstract xml:lang="eng">In the realm of social media monitoring and analysis, the availability of location-based information is of pivotal importance to understand the spatial behavior of social media users. Especially in fields like disaster management and urban planning, such data holds huge value for analysts and decision makers alike. However, as only few posts and messages in platforms like Twitter are already provided with GPS-coordinates or geo-tags by the users, researchers have proposed various algorithmic and modeldriven means to infer this information from properties like the content, network, or geographic history of the users. Since many of these methods only focus on isolated features or specific models, this paper presents a comprehensive framework that allows to integrate, combine and compare multiple geo-inference schemes in a unified, standardized, and performance-optimized fashion. In addition to that, we present a visual interface, which offers an intuitive, real-time assessment of the accuracy of singular and combined methods as well as support in detecting and understanding possible anomalies.We demonstrate the usefulness and relevance of our approach in a comprehensive case study.</dcterms:abstract>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/44251/1/Mahtal_2-rzgd1myf2nng1.pdf"/>
    <dc:creator>Koch, Steffen</dc:creator>
    <dc:creator>Lupu, Cristina</dc:creator>
    <dc:creator>Bechtold, Marvin</dc:creator>
    <dc:contributor>Mahtal, Sanae</dc:contributor>
    <dc:creator>Mahtal, Sanae</dc:creator>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:creator>Ertl, Thomas</dc:creator>
    <dc:contributor>Lupu, Cristina</dc:contributor>
  </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