Conference paper Open Access

Web Video Verification using Contextual Cues

Olga Papadopoulou; Markos Zampoglou; Symeon Papadopoulos; Yiannis Kompatsiaris

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Olga Papadopoulou</dc:creator>
  <dc:creator>Markos Zampoglou</dc:creator>
  <dc:creator>Symeon Papadopoulos</dc:creator>
  <dc:creator>Yiannis Kompatsiaris</dc:creator>
  <dc:description>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 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.</dc:description>
  <dc:subject>Video verification</dc:subject>
  <dc:subject>Context analysis</dc:subject>
  <dc:subject>Social media</dc:subject>
  <dc:subject>Fake news</dc:subject>
  <dc:title>Web Video Verification using Contextual Cues</dc:title>
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