Automatically converting visual content into textual description has long been a dream of a number of multimedia, computer vision and machine learning researchers. Image and video tagging, which has been studied heavily in recently years, can be regarded as a more realistic step to that ambitious goal. Especially, the explosion of media data and media users on the Internet, as well as the connections among users, among data and between users and data, bring us both challenges and opportunities. In this lecture, firstly we will review the evolution of multimedia tagging in the past decade, and then introduce state-of-the-art learning based tagging approaches, followed by summarizing manual tagging schemes on the Internet environment and presenting Internet-scale data-driven methods for scalable image and video tagging. Finally we will discuss the roles of models, data and users in multimedia tagging systems and study an sustainable ecosystem for multimedia tagging on the Internet environment. We will also discuss promising research and development directions in this area.