Misleading Metadata Detection on YouTube
This addresses the issue of misinformation on social media platforms, but it is incremental as it applies existing deep learning methods to a specific domain.
The paper tackles the problem of detecting misleading videos on YouTube, such as staged or morphed content, by developing UCNet, a deep network for supervised classification, achieving a macro averaged F-score of 0.82 on the FVC dataset compared to a baseline of 0.36.
YouTube is the leading social media platform for sharing videos. As a result, it is plagued with misleading content that includes staged videos presented as real footages from an incident, videos with misrepresented context and videos where audio/video content is morphed. We tackle the problem of detecting such misleading videos as a supervised classification task. We develop UCNet - a deep network to detect fake videos and perform our experiments on two datasets - VAVD created by us and publicly available FVC [8]. We achieve a macro averaged F-score of 0.82 while training and testing on a 70:30 split of FVC, while the baseline model scores 0.36. We find that the proposed model generalizes well when trained on one dataset and tested on the other.