In this paper, we argue that the ability to accurately spot random and social relationships in dynamic networks is essential to net- work applications that rely on human routines, such as, e.g., op- portunistic routing. We thus propose a strategy to analyze users' interactions in mobile networks where users act according to their interests and activity dynamics. Our strategy, named Random rElationship ClASsifier sTrategy (RECAST), allows classifying users' wireless interactions, separating random interactions from differ- ent kinds of social ties. To that end, RECAST observes how the real system differs from an equivalent one where entities' decisions are completely random. We evaluate the effectiveness of the RECAST classification on real-world user contact datasets collected in diverse networking contexts. Our analysis unveils significant dif- ferences among the dynamics of users' wireless interactions in the datasets, which we leverage to unveil the impact of social ties on opportunistic routing.