Dynamic OD estimators based on traffic measurements inevitably encounter the indeterminateness problem on the posterior OD flows as such systems structurally have more unknowns than constraints. To resolve this problem and take advantage of the emerging urban mobility data, the paper proposes a dynamic OD estimator based on location-based social networking (LBSN) data, leveraging the two-stage stochastic programming framework, under the assumption that similar check-in patterns are generated by the same OD pattern. The search space of the OD flows will be limited by integrating a batch of realizations/scenarios of the second-stage problem state (i.e., check-in pattern) in the model. The two-stage stochastic programming model decomposes in a master problem and a set of subproblems (one per scenario) via the Benders decomposition algorithm, which will be tackled alternately. The preliminary results from experiments conducted with the Foursquare data of Tokyo, Japan, show that the proposed OD estimator can effectively recurrent the check-in patterns and result in a good posterior OD estimate.