Conference paper Open Access

Learning to Detect Misleading Content on Twitter

Christina Boididou; Symeon Papadopoulos; Lazaros Apostolidis; Yiannis Kompatsiaris


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  <identifier identifierType="URL">https://zenodo.org/record/810537</identifier>
  <creators>
    <creator>
      <creatorName>Christina Boididou</creatorName>
      <affiliation>CERTH-ITI, Thessaloniki, Greece</affiliation>
    </creator>
    <creator>
      <creatorName>Symeon Papadopoulos</creatorName>
      <affiliation>CERTH-ITI, Thessaloniki, Greece</affiliation>
    </creator>
    <creator>
      <creatorName>Lazaros Apostolidis</creatorName>
      <affiliation>CERTH-ITI, Thessaloniki, Greece</affiliation>
    </creator>
    <creator>
      <creatorName>Yiannis Kompatsiaris</creatorName>
      <affiliation>CERTH-ITI, Thessaloniki, Greece</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Learning to Detect Misleading Content on Twitter</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2017</publicationYear>
  <subjects>
    <subject>social media</subject>
    <subject>verification</subject>
    <subject>fake detection</subject>
    <subject>news mining</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2017-06-08</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/810537</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1145/3078971.3078979</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;The publication and spread of misleading content is a problem of increasing magnitude, complexity and consequences in a world that is increasingly relying on user-generated content for news sourcing. To this end, multimedia analysis techniques could serve as an assisting tool to spot and debunk misleading content on the Web. In this paper, we tackle the problem of misleading multimedia content detection on Twitter in real time. We propose a number of new features and a new semi-supervised learning event adaptation approach that helps generalize the detection capabilities of trained models to unseen content, even when the event of interest is of different nature compared to that used for training. Combined with bagging, the proposed approach manages to outperform previous systems by a significant margin in terms of accuracy. Moreover, in order to communicate the verification process to end users, we develop a web-based application for visualizing the results.&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>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/FP7/610928/">610928</awardNumber>
      <awardTitle>REVEALing hidden concepts in Social Media</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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