Online clustering and classification for real-time event detection in Twitter

Event detection from social media is a challenging task due to the volume, the velocity and the variety of
user-generated data requiring real-time processing. Despite recent works on this subject, a generalized and scalable
approach that could be applied across languages and topics has not been consolidated, yet. In this paper, we propose
a methodology for real-time event detection from Twitter data that allows users to select a topic of interest by
defining a simple set of keywords and a matching rule. We implement the proposed methodology and evaluate it
with real data to detect different types of events.