Far beyond relationship topology, today's online social networks are also characterized by semantically rich text messages exchanged among users as well as GPS locations associated with those messages, as evidenced by Twitter's geotagged tweets. Textual contents help characterize users' personal interests, while geographical features help link users' behaviors in the online world to those in the physical world such as their mobility patterns. In this paper, instead of studying each aspect separately, as done by most previous works, we combine textual contents and spatial features in a joint way using Bayesian latent topic model in order to construct better algorithms for user characterization and social network study. Specifically, the integration of contents and spatial features in a user-centered environment can not only discover geographic topics but also enable the characterization of users' latent interests with geographic semantics. Such a novel characterization can be leveraged to benefit many interesting studies regarding social network heterogeneity and relationships between online networks and physical world. Using a large-scale twitter data set with broad geographical coverage, we systematically evaluate our framework in several typical inference tasks surrounding user, content and location, as well as carry out empirical studies in real world scenarios. Experimental results demonstrate the advantages of our joint modeling approach, as well as its potentials to facilitate user understanding, both in online world and physical world.