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Existing approaches to predict tie strength between users cover either online social networks or location-based social networks. However, there are no studies that combine these networks to unveil information about the intensity of the social relations between users. In this research paper we analyze aspects of tie strength – defined as partners and acquaintances – from two different domains: a location-based social network and an online social network for residents of Second Life. We compare user pairs according to their partnership and reveal significant differences between partners and acquaintances. Following these observations, we evaluate the social proximity of users with supervised and unsupervised learning algorithms and identified homophilic features as most valuable for the prediction of partnership.