Topic modeling methods such as eg Latent Dirichlet Allocation (LDA) are popular techniques to analyze large text corpora. With huge amounts of textual data that are collected over time in various fields of applied research, it becomes also relevant to be able to automatically monitor the evolution of topics identified from some sort of dynamic topic modeling approach. For this purpose, we propose a dynamic change detection method that relies on a rolling version of the classical LDA that allows for coherently modeled topics over time that are able to adapt to changing vocabulary. The changes are detected by assessing the intensity of word change in the LDA’s topics over time in comparison to the expected intensity of word change under stable conditions using resampling techniques. We apply our method to topics obtained by applying the RollingLDA to Covid-19 related news data from CNN and illustrate that the detected changes in these topics are well interpretable.