Event tracking is the task of discovering temporal patterns of events from text streams. Existing approaches for event tracking have two limitations: scalability and their inability to rule out non-relevant portions within texts in the stream 'relevant' to the event of interest. In this study, we propose a novel approach to tackle these limitations. To demonstrate our approach, we track news events across a collection of weblogs spanning a two-month time period. In particular we track variations in the intensity of discussion on a given event over time. We demonstrate that our model is capable of tracking both events and sub-events at a finer granularity. We also present in this paper our analysis of the blog dataset distributed by the conference organizers.