Conference paper Open Access
Olga Papadopoulou; Markos Zampoglou; Symeon Papadopoulos; Yiannis Kompatsiaris
<?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/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="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"><p>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.<br> 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.</p></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>
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