Twitter lists enable users of the social network to organize people they follow into groups of interest (e.g. politicians or journalists they like, favorite artists or athletes, authoritative figures in a given field, and so on). For the analyst, lists are a means of access to the structure of interactions between Twitter users and can be used to identify main actors of a field of interest. In this work, we introduce a methodology for constructing an edge-attributed multilayer network of Twitter users based on their membership to Twitter lists. We propose and validate a new approach that identifies local communities of users and their common interests from the constructed graph. We provide evidences that our method performs in a better way than global community detection approaches, and faster with as good results as competitive local methods.