We describe improved mechanisms to accurately classify days when news for topics receive unexpectedly high amount of coverage. We further investigate the factors which influence this classification using `Presidential Elections' as the topic of interest. This helps in bringing out useful trends and relations between days with hot topics by varying variables like history window size,van-ratio etc. We also propose a statistical scheme to approximate major events related to the topic. We then try to approximate the chain of events related to the major events. This can support a news alert service and also serve the purpose of automatically tracking news which follow up major events.