With the humongous amount of news stories published daily and the range of ways (RSS feeds, blogs etc) to disseminate them, even an expert at tracking new developing stories can feel the information overload. At most times, when a user is reading a news story, she would like to know "what happened before this?" or "how things progressed after this incident?". In this paper, we present a novel real-time yet simple method to detect and track new events related to violence and terrorism in news streams through their life over a time line. We do this by first extracting signature of the event, at microscopic level rather than topic or macroscopic level, and then tracking and linking this event with mentions of same event signature in other incoming news articles. There by forming a thread that links all the news articles that describe this specific event, with no training data used or machine learning algorithms employed. We also present our experimental evaluations conducted with Document Understand Conference (DUC) datasets that validate our observations and methodology.