Video Analysis with Image Recognition in TensorFlow

Video analytics, loosely defined as autonomous understanding of events occurring in a scene. The use of deceptive techniques in user-generated video portals is ubiquitous. Unscrupulous up loaders deliberately mislabel video descriptors aiming at increasing their views and subsequently their ad revenue. This problem, usually referred to as "click-bait," may severely undermine user experience. In this work, we study the click-bait problem on YouTube by collecting metadata for 206k videos. To address it, we devise a deep learning model based on variation auto-encoders that supports the diverse modalities of data that videos include. Every click earns them display advertisement revenue. Social media users who are tricked into clicking may experience a sense of disappointment or agitation, and social media operators have been observing growing amounts of click bait on their platforms. As largest video-sharing platform on the web, YouTube, too, suffers from click bait. Thus, it is susceptible to recommending misleading videos to users.