Conference paper Open Access
Olga Papadopoulou; Markos Zampoglou; Symeon Papadopoulos; Yiannis Kompatsiaris
<?xml version='1.0' encoding='UTF-8'?> <record xmlns="http://www.loc.gov/MARC21/slim"> <leader>00000nam##2200000uu#4500</leader> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">Video verification</subfield> </datafield> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">Context analysis</subfield> </datafield> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">Social media</subfield> </datafield> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">Fake news</subfield> </datafield> <controlfield tag="005">20200120173813.0</controlfield> <controlfield tag="001">810474</controlfield> <datafield tag="711" ind1=" " ind2=" "> <subfield code="d">June 6, 2017</subfield> <subfield code="g">MFSec 2017 @ ICMR2017</subfield> <subfield code="a">ACM 2nd International Workshop on Multimedia Forensics and Security, co-located with the 2017 ACM International Conference on Multimedia Retrieval</subfield> <subfield code="c">Bucharest, Romania</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">CERTH-ITI, Thessaloniki, Greece</subfield> <subfield code="a">Markos Zampoglou</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">CERTH-ITI, Thessaloniki, Greece</subfield> <subfield code="a">Symeon Papadopoulos</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">CERTH-ITI, Thessaloniki, Greece</subfield> <subfield code="a">Yiannis Kompatsiaris</subfield> </datafield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="s">657564</subfield> <subfield code="z">md5:2804d1e7f39e1c65f5b02b336d00088e</subfield> <subfield code="u">https://zenodo.org/record/810474/files/web-video-verification.pdf</subfield> </datafield> <datafield tag="542" ind1=" " ind2=" "> <subfield code="l">open</subfield> </datafield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="y">Conference website</subfield> <subfield code="u">http://mklab.iti.gr/mfsec2017/</subfield> </datafield> <datafield tag="260" ind1=" " ind2=" "> <subfield code="c">2017-06-06</subfield> </datafield> <datafield tag="909" ind1="C" ind2="O"> <subfield code="p">openaire</subfield> <subfield code="p">user-invid-h2020</subfield> <subfield code="o">oai:zenodo.org:810474</subfield> </datafield> <datafield tag="100" ind1=" " ind2=" "> <subfield code="u">CERTH-ITI, Thessaloniki, Greece</subfield> <subfield code="a">Olga Papadopoulou</subfield> </datafield> <datafield tag="245" ind1=" " ind2=" "> <subfield code="a">Web Video Verification using Contextual Cues</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">user-invid-h2020</subfield> </datafield> <datafield tag="536" ind1=" " ind2=" "> <subfield code="c">687786</subfield> <subfield code="a">In Video Veritas – Verification of Social Media Video Content for the News Industry</subfield> </datafield> <datafield tag="540" ind1=" " ind2=" "> <subfield code="u">https://creativecommons.org/licenses/by/4.0/legalcode</subfield> <subfield code="a">Creative Commons Attribution 4.0 International</subfield> </datafield> <datafield tag="650" ind1="1" ind2="7"> <subfield code="a">cc-by</subfield> <subfield code="2">opendefinition.org</subfield> </datafield> <datafield tag="520" ind1=" " ind2=" "> <subfield code="a"><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></subfield> </datafield> <datafield tag="024" ind1=" " ind2=" "> <subfield code="a">10.1145/3078897.3080535</subfield> <subfield code="2">doi</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">publication</subfield> <subfield code="b">conferencepaper</subfield> </datafield> </record>
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