Many complex networks are characterized by a community structure, ie the presence of groups of nodes that are more densely connected with each other than with the rest of the network. The algorithms developed to detect such communities usually consider the structural properties of the network, ie the static links between nodes. In the case of social networks this means considering, for example,“friendship” links on Facebook or “followers” on Twitter. We argue that these kinds of static links are not indicative of the real community structure underlying these networks, since users of social media usually have hundreds of connections even with people they are only acquaintances with. Moreover, users of social media typically only communicate with a subset of these connections, and form real communities only within these subsets. In this paper, we adapt standard community detection algorithms to account for this