A novel framework to address the link prediction problem in multiplex social networks is introduced. In this framework, uncertainty found in social data due to noise, missing information and observation errors is handled by the belief function theory. Despite the numerous published studies on link prediction, few research are concerned with social data imperfections issues which cause distortions in social networks structures and probably inaccurate results. In addition, most works focus on similarity scores based on network topology whereas social networks include rich content which may add semantic to the analysis and enhance results. To this end, we develop a link prediction method that combine network topology and social content to predict new links existence along with their types in multiplex social networks. Structural and social neighbors information are gathered and pooled using belief function theory combination rules. It is subsequently revised according to global information about the multiplex. Experiments performed on real world social data show that our approach works well and enhances the prediction accuracy.