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Web Video Verification using Contextual Cues
| Content Provider | ACM Digital Library |
|---|---|
| Author | Kompatsiaris, Yiannis Zampoglou, Markos Papadopoulou, Olga Papadopoulos, Symeon |
| Abstract | 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. |
| Starting Page | 6 |
| Ending Page | 10 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781450350341 |
| DOI | 10.1145/3078897.3080535 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2017-06-06 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Context analysis Social media Video verification Fake news |
| Content Type | Text |
| Resource Type | Article |
