publication . Conference object . 2017

Web Video Verification using Contextual Cues

Zampoglou, Markos; Vrochidis, Stefanos; Rocha, Anderson; Katos, Vasilis; Papadopoulou, Olga; Zampoglou, Markos; Papadopoulos, Symeon; Kompatsiaris, Yiannis;
Open Access
  • Published: 06 Jun 2017
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. 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 ...
Persistent Identifiers
free text keywords: Video verification, Context analysis, Social media, Fake news, Metadata, Complementarity (molecular biology), Fusion scheme, Feature set, Classifier (linguistics), World Wide Web, Computer science, Supervised learning
Funded by
In Video Veritas – Verification of Social Media Video Content for the News Industry
  • Funder: European Commission (EC)
  • Project Code: 687786
  • Funding stream: H2020 | IA
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Conference object . 2017
Provider: ZENODO
Conference object . 2017
Provider: Crossref
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