Detecting and Summarizing Emergent Events in Microblogs and Social Media Streams by Dynamic Centralities
This addresses the need for real-time event detection in social media for applications like public security, though it appears incremental as it builds on existing methods for keyword detection and summarization.
The paper tackles the problem of detecting and summarizing emergent events in social media streams by proposing a system that uses dynamic eigenvector centrality for keyword ranking and a set cover algorithm for summarization, demonstrating it on Twitter data related to public security events.
Methods for detecting and summarizing emergent keywords have been extensively studied since social media and microblogging activities have started to play an important role in data analysis and decision making. We present a system for monitoring emergent keywords and summarizing a document stream based on the dynamic semantic graphs of streaming documents. We introduce the notion of dynamic eigenvector centrality for ranking emergent keywords, and present an algorithm for summarizing emergent events that is based on the minimum weight set cover. We demonstrate our system with an analysis of streaming Twitter data related to public security events.