- In this paper, we design and analyze information spreading algorithms for dynamic networks with correlated data. In these networks, either the data to be distributed, the data already available at the nodes, or both are correlated. Moreover, nodes' availability and connectivity is dynamic - a scenario typical for wireless networks. Our contribution is twofold. First, although coding schemes for correlated data have been studied extensively, the focus has been on characterizing the rate region in static networks. In an information spreading scheme, however, nodes may communicate by continuously exchanging packets according to some underlying communication model. The main figure of merit is the stopping time - the time required until nodes can successfully decode. While information spreading schemes, such as gossip, are practical, distributed, and scalable, they have only been studied for uncorrelated data. We close this gap by providing techniques to analyze network-coded information spreading in dynamic networks with correlated data. Second, we give a clean framework for oblivious dynamic network models that in particular applies to a multitude of wireless network and communication scenarios. We specify a general setting for the data model and give tight bounds on the stopping times of network-coded protocols in this wide range of settings. En route, we analyze the capacities seen by nodes under a network-coded information spreading protocol, a previously unexplored question. We conclude with extensive simulations, clearly validating the key trends and phenomena predicted in the analysis.