Towards Annotating Media Contents through Social Diffusion Analysis

Recently, the boom of media contents on the Internet raises challenges in managing them effectively and thus requires automatic media annotation techniques. Motivated by the observation that media contents are usually shared frequently in online communities and thus have a lot of social diffusion records, we propose a novel media annotating approach depending on these social diffusion records instead of metadata. The basic assumption is that the social diffusion records reflect the common interests (CI) between users, which can be analyzed for generating annotations. With this assumption, we present a novel CI-based social diffusion model and translate the automatic annotating task into the CI-based diffusion maximization (CIDM) problem. Moreover, we propose to solve the CIDM problem through two optimization tasks, corresponding to the training and test stages in supervised learning. Extensive experiments on real-world data sets show that our approach can effectively generate high quality annotations, and thus demonstrate the capability of social diffusion analysis in annotating media.