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The current trend in social media analysis and application is to use the pre-defined features and devoted to the later model development modules to meet the end tasks. In this work, we claim that representation is critical to the end tasks and contributes much to the model development module. We provide evidence that specially learned feature well addresses the diverse, heterogeneous and collective characteristics of social media data. Therefore, we propose to transfer the focus from the model development to latent feature learning, and present a general feature learning framework based on the popular deep architecture. In particular, following the proposed framework, we design a novel relational generative deep learning model to test the idea on link analysis tasks in the social media networks. We show that the derived latent features well embed both the media content and their observed links, leading to improvement in social media tasks of user recommendation and social image annotation.