Video quality of User Generated Content (UGC) is extremely difficult to wrangle with due to their high diversity of contents and quality. They bring new challenges to how we traditionally measured and assessed video quality. Most videos uploaded to YouTube, and other video sharing platforms, are UGC. To facilitate and encourage research in UGC compression and quality assessment, in 2019 we released a large scale UGC dataset (YT-UGC) that contained representative UGC raw videos along with their ground truth Mean Opinion Score (MOS), Differential MOS (DMOS), and content labels. Parallely, we have been investigating a number of efforts analyzing and optimizing UGC video quality. Recently, we built a novel deep learning based framework to understand the importance of content, technical quality, and compression level on perceptual quality. In this talk, we will walk through our video analysis framework, our latest DNN-based video quality metric called YouVQ, and present some results.