We report on an investigation of transitions among the topics of pages visited by a sample population of users of MSN Search. We learn probabilistic models of topic transitions for individual users and groups of users. In an effort to compare topic transitions for individuals versus larger groups, we consider the relative accuracies of personal models of topic dynamics with models constructed from sets of pages drawn from similar groups and from a larger population of users. To explore temporal dynamics, we compare the accuracy of these models for predicting transitions in the topics of visits at increasingly more distant times in the future. Finally, we touch on promising directions for applying models of search topic dynamics.