Cultural modeling aims at developing behavioral models of groups and analyzing the impact of culture factors on group behavior using computational methods. Machine learning methods in particular classification, play a central role in such applications. In modeling cultural data, it is expected that standard classifiers yield good performance under the assumptions that class distribution is balanced and different classification errors have uniform costs. However, these assumptions are often violated in practice and thus the performance of standard classifiers is severely hindered. To handle this problem, this paper studies cost-sensitive learning in cultural modeling domain by considering cost factor when building the classifiers, with the aim of minimizing total misclassification costs. We empirically investigate four typical cost-sensitive learning methods, combine them with six standard classifiers and evaluate their performances under various conditions. Our empirical study verifies the effectiveness of cost-sensitive learning in cultural modeling. Based on the results of our experimental study, we gain a thorough insight into the problem of class imbalance and non-uniform misclassification costs, as well as the selection of cost-sensitive methods, base classifiers and method-classifier pairs for this domain.