This talk will outline preliminary results from using graphs to monitor streaming text. In particular, we consider news articles where the text is evolving in time. There are several obstacles to analyzing streaming text data that do not arise when the corpus of documents is static. When employing a vector space model for text processing, word weights are usually used which depend on the frequency of the word in the document and the frequency of the word in the corpus. Such word weighting must be revised in order to process a streaming collection of documents. This presentation discusses some methods for word weighting streaming text as well as methods for representing a changing corpus with a dynamic graph.