  Tweets about everyday events are published on Twitter. Detecting such events
is a challenging task due to the diverse and noisy contents of Twitter. In this
paper, we propose a novel approach named Weighted Dynamic Heartbeat Graph
(WDHG) to detect events from the Twitter stream. Once an event is detected in a
Twitter stream, WDHG suppresses it in later stages, in order to detect new
emerging events. This unique characteristic makes the proposed approach
sensitive to capture emerging events efficiently. Experiments are performed on
three real-life benchmark datasets: FA Cup Final 2012, Super Tuesday 2012, and
the US Elections 2012. Results show considerable improvement over existing
event detection methods in most cases.
