Publikation:

SocialOcean : Visual Analysis and Characterization of Social Media Bubbles

Lade...
Vorschaubild

Dateien

Zu diesem Dokument gibt es keine Dateien.

Datum

2018

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

Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published

Erschienen in

2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA). Piscataway, NJ: IEEE, 2018. ISBN 978-1-5386-9194-6. Available under: doi: 10.1109/BDVA.2018.8534023

Zusammenfassung

Social media allows citizens, corporations, and authorities to create, post, and exchange information. The study of its dynamics will enable analysts to understand user activities and social group characteristics such as connectedness, geospatial distribution, and temporal behavior. In this context, social media bubbles can be defined as social groups that exhibit certain biases in social media. These biases strongly depend on the dimensions selected in the analysis, for example, topic affinity, credibility, sentiment, and geographic distribution. In this paper, we present SocialOcean, a visual analytics system that allows for the investigation of social media bubbles. There exists a large body of research in social sciences which identifies important dimensions of social media bubbles (SMBs). While such dimensions have been studied separately, and also some of them in combination, it is still an open question which dimensions play the most important role in defining SMBs. Since the concept of SMBs is fairly recent, there are many unknowns regarding their characterization. We investigate the thematic and spatiotemporal characteristics of SMBs and present a visual analytics system to address questions such as: What are the most important dimensions that characterize SMBs? and How SMBs embody in the presence of specific events that resonate with them? We illustrate our approach using three different real scenarios related to the single event of Boston Marathon Bombing, and political news about Global Warming. We perform an expert evaluation, analyze the experts' feedback, and present the lessons learned.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
004 Informatik

Schlagwörter

geodata, Social Media Bubbles, VGI

Konferenz

2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA), 17. Okt. 2018 - 19. Okt. 2018, Konstanz, Germany
Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Zitieren

ISO 690DIEHL, Alexandra, Michael HUNDT, Johannes HÄUSSLER, Daniel SEEBACHER, Siming CHEN, Nida CILASUN, Daniel A. KEIM, Tobias SCHRECK, 2018. SocialOcean : Visual Analysis and Characterization of Social Media Bubbles. 2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA). Konstanz, Germany, 17. Okt. 2018 - 19. Okt. 2018. In: 2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA). Piscataway, NJ: IEEE, 2018. ISBN 978-1-5386-9194-6. Available under: doi: 10.1109/BDVA.2018.8534023
BibTex
@inproceedings{Diehl2018Socia-44990,
  year={2018},
  doi={10.1109/BDVA.2018.8534023},
  title={SocialOcean : Visual Analysis and Characterization of Social Media Bubbles},
  isbn={978-1-5386-9194-6},
  publisher={IEEE},
  address={Piscataway, NJ},
  booktitle={2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA)},
  author={Diehl, Alexandra and Hundt, Michael and Häußler, Johannes and Seebacher, Daniel and Chen, Siming and Cilasun, Nida and Keim, Daniel A. and Schreck, Tobias}
}
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/123456789/44990">
    <dc:contributor>Cilasun, Nida</dc:contributor>
    <dcterms:abstract xml:lang="eng">Social media allows citizens, corporations, and authorities to create, post, and exchange information. The study of its dynamics will enable analysts to understand user activities and social group characteristics such as connectedness, geospatial distribution, and temporal behavior. In this context, social media bubbles can be defined as social groups that exhibit certain biases in social media. These biases strongly depend on the dimensions selected in the analysis, for example, topic affinity, credibility, sentiment, and geographic distribution. In this paper, we present SocialOcean, a visual analytics system that allows for the investigation of social media bubbles. There exists a large body of research in social sciences which identifies important dimensions of social media bubbles (SMBs). While such dimensions have been studied separately, and also some of them in combination, it is still an open question which dimensions play the most important role in defining SMBs. Since the concept of SMBs is fairly recent, there are many unknowns regarding their characterization. We investigate the thematic and spatiotemporal characteristics of SMBs and present a visual analytics system to address questions such as: What are the most important dimensions that characterize SMBs? and How SMBs embody in the presence of specific events that resonate with them? We illustrate our approach using three different real scenarios related to the single event of Boston Marathon Bombing, and political news about Global Warming. We perform an expert evaluation, analyze the experts' feedback, and present the lessons learned.</dcterms:abstract>
    <dc:creator>Hundt, Michael</dc:creator>
    <dc:contributor>Seebacher, Daniel</dc:contributor>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:contributor>Häußler, Johannes</dc:contributor>
    <dcterms:issued>2018</dcterms:issued>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2019-02-12T12:22:27Z</dc:date>
    <dc:creator>Häußler, Johannes</dc:creator>
    <dc:creator>Schreck, Tobias</dc:creator>
    <dc:contributor>Diehl, Alexandra</dc:contributor>
    <dc:creator>Keim, Daniel A.</dc:creator>
    <dcterms:title>SocialOcean : Visual Analysis and Characterization of Social Media Bubbles</dcterms:title>
    <dc:contributor>Chen, Siming</dc:contributor>
    <dc:creator>Seebacher, Daniel</dc:creator>
    <dc:contributor>Keim, Daniel A.</dc:contributor>
    <dc:contributor>Schreck, Tobias</dc:contributor>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2019-02-12T12:22:27Z</dcterms:available>
    <dc:creator>Chen, Siming</dc:creator>
    <dc:creator>Cilasun, Nida</dc:creator>
    <dc:language>eng</dc:language>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/44990"/>
    <dc:creator>Diehl, Alexandra</dc:creator>
    <dc:contributor>Hundt, Michael</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