June 6, 2017 Conference paper Open Access

Olga Papadopoulou; Markos Zampoglou; Symeon Papadopoulos; Yiannis Kompatsiaris

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  <identifier identifierType="URL">https://zenodo.org/record/810474</identifier>
  <creators>
    <creator>
      <creatorName>Olga Papadopoulou</creatorName>
      <affiliation>CERTH-ITI, Thessaloniki, Greece</affiliation>
    </creator>
    <creator>
      <creatorName>Markos Zampoglou</creatorName>
      <affiliation>CERTH-ITI, Thessaloniki, Greece</affiliation>
    </creator>
    <creator>
      <creatorName>Symeon Papadopoulos</creatorName>
      <affiliation>CERTH-ITI, Thessaloniki, Greece</affiliation>
    </creator>
    <creator>
      <creatorName>Yiannis Kompatsiaris</creatorName>
      <affiliation>CERTH-ITI, Thessaloniki, Greece</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Web Video Verification using Contextual Cues</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2017</publicationYear>
  <subjects>
    <subject>Video verification</subject>
    <subject>Context analysis</subject>
    <subject>Social media</subject>
    <subject>Fake news</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2017-06-06</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/810474</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1145/3078897.3080535</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/invid-h2020</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://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;As news agencies and the public increasingly rely on User-Generated Content, content verification is vital for news producers and consumers alike. We present a novel approach for verifying Web videos by analyzing their online context. It is based on supervised learning on contextual features: one feature set is based on an existing approach for tweet verification adapted to video comments. The other is based on video metadata, such as the video description, likes/dislikes, and uploader information.&lt;br&gt;
We evaluate both on a dataset of real and fake videos from YouTube, and demonstrate their effectiveness (F-scores: 0.82, 0.79). We then explore their complementarity and show that under an optimal fusion scheme, the classifier would reach an F-score of 0.9. We finally study the performance of the classifier through time, as more comments accumulate, emulating a real-time verification setting.&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>
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