Conference paper Open Access

Video Retrieval for Multimedia Verification of Breaking News on Social Networks

Nixon, Lyndon; Zhu, Shu; Fischer, Fabian; Rafelsberger, Walter; Gobel, Max; Scharl, Arno


DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="URL">https://zenodo.org/record/1054120</identifier>
  <creators>
    <creator>
      <creatorName>Nixon, Lyndon</creatorName>
      <givenName>Lyndon</givenName>
      <familyName>Nixon</familyName>
      <affiliation>MODUL Technology</affiliation>
    </creator>
    <creator>
      <creatorName>Zhu, Shu</creatorName>
      <givenName>Shu</givenName>
      <familyName>Zhu</familyName>
      <affiliation>MODUL Technology</affiliation>
    </creator>
    <creator>
      <creatorName>Fischer, Fabian</creatorName>
      <givenName>Fabian</givenName>
      <familyName>Fischer</familyName>
      <affiliation>MODUL Technology</affiliation>
    </creator>
    <creator>
      <creatorName>Rafelsberger, Walter</creatorName>
      <givenName>Walter</givenName>
      <familyName>Rafelsberger</familyName>
      <affiliation>webLyzard technology</affiliation>
    </creator>
    <creator>
      <creatorName>Gobel, Max</creatorName>
      <givenName>Max</givenName>
      <familyName>Gobel</familyName>
      <affiliation>webLyzard technology</affiliation>
    </creator>
    <creator>
      <creatorName>Scharl, Arno</creatorName>
      <givenName>Arno</givenName>
      <familyName>Scharl</familyName>
      <affiliation>webLyzard technology</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Video Retrieval for Multimedia Verification of Breaking News on Social Networks</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2017</publicationYear>
  <subjects>
    <subject>Video Retrieval</subject>
    <subject>Social Network Retrieval</subject>
    <subject>Social Media Retrieval</subject>
    <subject>Social Media Extraction</subject>
    <subject>Breaking News Detection</subject>
    <subject>Story Detection</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2017-10-27</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/1054120</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1145/3132384.3132386</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/invid-h2020</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;This paper presents an approach to automatically detecting breaking news events from social media streams, using event detection to collect in near real time relevant video documents from social networks regarding that breaking news. A visual analytics dashboard provides access to the results of the content processing pipeline, providing a rich interactive interface to explore emerging stories and select video material around those stories for veri€cation.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/687786/">687786</awardNumber>
      <awardTitle>In Video Veritas – Verification of Social Media Video Content for the News Industry</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
51
28
views
downloads
Views 51
Downloads 28
Data volume 19.3 MB
Unique views 49
Unique downloads 24

Share

Cite as