Social networking services (SNSs) bring a new dimension to social life that helps people to easily communicate with each other. As one of the representative products of SNSs, YouTube has gained in popularity since 2005. Today, YouTube is the most well-known video delivery service on the Internet. As a result, YouTube data analysis has become increasingly important for understanding the characteristics of online video sites, which is vital in developing efficient content distribution systems.
In this thesis, we examine the characteristics of YouTube data. Our analysis is based on an empirical data gathered from the live YouTube service. In addition to the basic statistics, correlational statistics have been also conducted. The results of basic statistics show an overview of YouTube data and the correlational statistics display the relationship among users and videos. Furthermore, Friend-Commenter Ratio (FCR) and Shortest Responses Time (SRT) have been measured. To the best of our knowledge, this is the first time that these two new metrics are proposed and analyzed.
