Real-time Event Detection on Social Data Streams
This addresses the problem of immediate insights into ongoing events for users of social platforms, but it is incremental as it builds on existing clustering methods with a focus on scale and speed.
The paper tackles real-time event detection on social data streams by modeling events as clusters of trending entities over time, achieving a system that processes millions of entities per minute and produces dynamically updated events. It builds an evaluation dataset from Twitter Firehose and proposes novel metrics for clustering quality.
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.