Owing to the widespread of the Internet, it is easier to reach information through the Internet. When performing environment scanning, organizations typically deal with a numerous of episodes and events about their core business, relevant technique standards, competitors, and market, among many others, where each episode or event to monitor or track generally is associated with many news documents. To reduce such information overload and information fatigues when monitoring or tracking events, it is essential to develop an effective episode evolution discovery technique to organize all news documents pertaining to an event of interest into an episode evolution graph. In this thesis, we propose a new feature selection metric, referred to as TF2 and develop an episode evolution discovery technique that uses the TF2 metric as its feature selection method and TFIDF as the document representation scheme. Using the traditional TFIDF as the performance benchmark, our empirical evaluation results suggest that our proposed TF2 technique outperforms its benchmarks and demonstrates the utility of TF2 metric for discovering episode evolution relationships.