Variations and Trends in Hot Topics in News Feeds

Abstract

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.