  Social networks are quickly becoming the primary medium for discussing what
is happening around real-world events. The information that is generated on
social platforms like Twitter can produce rich data streams for immediate
insights into ongoing matters and the conversations around them. To tackle the
problem of event detection, we model events as a list of clusters of trending
entities over time. We describe a real-time system for discovering events that
is modular in design and novel in scale and speed: it applies clustering on a
large stream with millions of entities per minute and produces a dynamically
updated set of events. In order to assess clustering methodologies, we build an
evaluation dataset derived from a snapshot of the full Twitter Firehose and
propose novel metrics for measuring clustering quality. Through experiments and
system profiling, we highlight key results from the offline and online
pipelines. Finally, we visualize a high profile event on Twitter to show the
importance of modeling the evolution of events, especially those detected from
social data streams.
