A ranking algorithm for user-generated video contents based on social activities

People upload extremely large number of user-generated contents everyday and participate in various social activities regarding with contents on the Web. Thus, researchers develop efficient user-generated content management systems. Typical social activities are to add favorite list, give rating, subscribe, and give comments. We observe that we can use these activities for evaluating the value of contents. Based on this observation, we introduce a ranking algorithm for user-generated video contents. We make use of collective intelligence from social activities and the algorithm finds influential users, calculates the value of contents, and orders the contents by statistical method.