YouTree: A Visualization Paradigm of Statistically and Textually Similar Videos

Abstract

The rise of social media usage in the form of multimedia is on an exponential increase owing to the increased internet bandwidths in the recent past. As a result, people communicate in the form of videos and images a lot more than ever. One such video sharing and content developer platform is YouTube. YouTube has many features on video analytics in the form of recommendation systems, monetisation etc. It also offers many features for developers to evaluate their content and offers insights on the performance of their videos. Though these features are available, there is not even a single feature for developers to evaluate their content based on the performance of other's videos, which share the same nature of the content - the similarity between any two videos. Here, the similarity between two videos has a statistical measure apart from the content, which includes description and comments of a video. Thus, we propose an analysis of a query video and a range of videos to determine the most similar videos using statistical and textual similarity. The statistical similarity is evident from the number of derived features extracted from a video and the textual similarity is found by analysing the text data from the description and comments of a video. Experimental results show that the resultant similar videos are highly representative of both the statistical and textual similarity and can be used as a measure to compare two videos.