Given the plethora of social networking sites, it can be difficult for users to browse too many sites and discover social friends. For example, for a new diabetes patient, how can s/he find the users with similar symptoms on different dedicated sites and form supporting groups with them? Since different sites may use different vocabularies, this problem is challenging to match users across different sites. To address it, in this paper, we present a tool to demonstrate how to construct a virtual social network across multiple social networking sites. Specifically, it uses bipartite graphs to represent the relation ships between users and their posts' keywords in each site, it bridges the gap between different vocabularies of different sites based on their semantic relatedness through concept-based interpretations, and it uses an efficient propagation algorithm to obtain the similarity between users from different sites, which can be used to construct the cross-site virtual social network.