Personalizing of Content Dissemination in Online Social Networks

Online social networks have seen a rapid growth in recent years. A key aspect of many of such networks is that they are rich in content and social interactions. Users of social networks connect with each other and forming their own communities. With the evolution of huge communities hosted by such websites, users suffer from managing information overload and it is become hard to extract useful information. Thus, users need a mechanism to filter online social streams they receive as well as enable them to interact with most similar users. In this paper, we address the problem of personalizing dissemination of relevant information in knowledge sharing social network. The proposed framework identifies the most appropriate user(s) to receive specific post by calculating similarity between target user and others. Similarity between users within OSN is calculated based on users’ social activity which is an integration of content published as well as social pattern Application of this framework to a representative subset of a large real-world social network: the user/community network of the blog service stack overflow is illustrated here. Experiments show that the proposed model outperform tradition similarity methods. Keywords—social network; content similarity measurement; Information retrieval; Information dissemination