SocialOcean : Visual Analysis and Characterization of Social Media Bubbles

No Thumbnail Available
Files
There are no files associated with this item.
Date
2018
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
oops
EU project number
Project
Open Access publication
Restricted until
Title in another language
Research Projects
Organizational Units
Journal Issue
Publication type
Contribution to a conference collection
Publication status
Published
Published in
2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA). - Piscataway, NJ : IEEE, 2018. - ISBN 978-1-5386-9194-6
Abstract
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.
Summary in another language
Subject (DDC)
004 Computer Science
Keywords
geodata, Social Media Bubbles, VGI
Conference
2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA), Oct 17, 2018 - Oct 19, 2018, Konstanz, Germany
Review
undefined / . - undefined, undefined. - (undefined; undefined)
Cite This
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, Oct 17, 2018 - Oct 19, 2018. In: 2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA). Piscataway, NJ:IEEE. 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>
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