# Real-time Event Detection on Social Data Streams

## TL;DR

This paper presents a real-time, scalable system for detecting and tracking events on social media streams like Twitter, using clustering methods and novel evaluation metrics to analyze trending entities and their evolution.

## Contribution

It introduces a modular, high-speed event detection system that handles millions of entities per minute and proposes new metrics for clustering quality assessment.

## Key findings

- System can process millions of entities per minute.
- Proposed new metrics improve clustering evaluation.
- Visualization demonstrates event evolution tracking.

## Abstract

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.