Abstract
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.
Highlights
IntroductionSearching for event information in Twitter using keywords is a naive way, where the keyword-based search returns relevant documents matched to queried keywords
Weighted Dynamic Heartbeat Graph (WDHG) Series Instead of trying to compute the features directly from the text stream, we developed a flexible approach by capturing the co-occurrence relationship among the words in the form of a graph series
The text stream was systematically transformed into a series of temporal graphs
Summary
Searching for event information in Twitter using keywords is a naive way, where the keyword-based search returns relevant documents matched to queried keywords. One simple way to find event-related information is to use trending keywords suggested by Twitter. Trending keywords may not necessarily identify all the topics required
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