UCLA

Content creators on YouTube face two related issues. First, after publishing a video, theywould like an accurate prediction of its future views. Second, prior to publishing a video, they would like recommendations on setting key video attributes in order to achieve a desired growth pattern in the video’s views. This thesis addresses the first issue by fitting time series models to video views across a diverse selection of YouTube videos. On average, the fitted models can use the first two days of a video’s lifetime to predict the following 24 hours with a 5% absolute percent error. This thesis addresses the second issue by using a probabilistic classifier to identify video attributes likely to produce favorable view growth patterns. The duration, number of characters in the title, and dominant color of the thumbnail are identified as the best indicators of the video view growth pattern.