In recent years, a large amount of information has become available online in the form of web documents, social networks, blogs, or other kinds of social entities. Such networks are large, heterogeneous, and often contain a huge number of links. This linkage structure encodes rich structural information about the underlying topical behavior of the network. Such networks are often dynamic and evolve rapidly over time. Much of the work in the literature has focussed either on the problem of classification with purely text behavior, or on the problem of classification with purely the linkage behavior of the underlying graph. Furthermore, the work in the literature is mostly designed for the problem of static networks. However, a given network may be quite diverse, and the use of either content or structure could be more or less effective in different parts of the network. In this paper, we examine the problem of node classification in dynamic information networks with both text content and links. Our techniques use a random walk approach in conjunction with the content of the network in order to facilitate an effective classification process. This results in an effective approach which is more robust to variations in content and linkage structure. Our approach is dynamic, and can be applied to networks which are updated incrementally. Our results suggest that an approach which is based on a combination of content and links is extremely robust and effective. We present experimental results illustrating the effectiveness and efficiency of our approach. Copyright © SIAM.