Today, social networks are for much more than just having fun with friends. Millions of dollars are being spent to extract valuable information out of social networks for marketing purposes. Attaching machine readable semantics to social networks, that is absent at present, will lead to a better degree of information extraction. To achieve this goal, user input is required. From users’ point of view, providing the same data to different social networks creates a drawback. This is due to the centralized one-of-a-kind nature of social networks. Social networks do not return users’ data. In this paper, we exposed proposal and development of a framework toward data reusability and portability across social networks. The proposed framework is built upon “pull” strategy, although “push” strategy is shortly discussed. Test case developed to practically measure the feasibility of the proposed framework will be subject of our discussion. This study was linked to existing research and conclusions were drawn for further development.