June 6, 2017 Conference paper Open Access

Olga Papadopoulou; Markos Zampoglou; Symeon Papadopoulos; Yiannis Kompatsiaris

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{
  "DOI": "10.1145/3078897.3080535", 
  "author": [
    {
      "family": "Olga Papadopoulou"
    }, 
    {
      "family": "Markos Zampoglou"
    }, 
    {
      "family": "Symeon Papadopoulos"
    }, 
    {
      "family": "Yiannis Kompatsiaris"
    }
  ], 
  "issued": {
    "date-parts": [
      [
        2017, 
        6, 
        6
      ]
    ]
  }, 
  "abstract": "<p>As news agencies and the public increasingly rely on User-Generated Content, content verification is vital for news producers and consumers alike.\u00a0We 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>\nWe evaluate both on a dataset of real and fake videos from YouTube, and demonstrate their effectiveness (F-scores: 0.82, 0.79).\u00a0We 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>", 
  "title": "Web Video Verification using Contextual Cues", 
  "type": "paper-conference", 
  "id": "810474"
}