Over the last few years, blogs (web logs) have gained massive popularity and have become one of the most influential web social media in our times. Every blog post in the Blogosphere has a well defined timestamp, which is not taken into account by search engines. By conducting research regarding this feature of the Blogosphere, we can attempt to discover bursty terms and correlations between them during a time interval. We apply Kleinberg's automaton on extracted titles of blog posts to discover bursty terms, we introduce a novel representation of a term's burstiness evolution called State Series and we employ a Euclidean-based distance metric to discover potential correlations between terms without taking into account their context. We evaluate the results trying to match them with real life events. Finally, we propose some ideas for further evaluation techniques and future research in the field. Categories and Subject Descriptors H.4.m. [Information Systems Applications]: ...