Olga Papadopoulou
Markos Zampoglou
Symeon Papadopoulos
Yiannis Kompatsiaris
2017-06-06
<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>
https://doi.org/10.1145/3078897.3080535
oai:zenodo.org:810474
Zenodo
https://zenodo.org/communities/invid-h2020
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
MFSec 2017 @ ICMR2017, ACM 2nd International Workshop on Multimedia Forensics and Security, co-located with the 2017 ACM International Conference on Multimedia Retrieval, Bucharest, Romania, June 6, 2017
Video verification
Context analysis
Social media
Fake news
Web Video Verification using Contextual Cues
info:eu-repo/semantics/conferencePaper