Identifying, Attributing and Describing Spatial Bursts

User generated content that appears on weblogs, wikis and social networks has been increasing at an unprecedented rate. The wealth of information produced by individuals from different geographical locations presents a challenging task of intelligent processing. In this paper, we introduce a methodology to identify notable geographically focused events out of this collection of user generated information. At the heart of our proposal lie efficient algorithms that identify geographically focused information bursts, attribute them to demographic factors and identify sets of descriptive keywords. We present the results of a prototype evaluation of our algorithms on BlogScope, a large-scale social media warehousing platform. We demonstrate the scalability and practical utility of our proposal running on top of a multi-terabyte text collection.

• Type: Journal
• Year: 2010
• Where: PVLDB
• Authors: Michael Mathioudakis, Nilesh Bansal, Nick Koudas
• Keywords: Large-scale Social Media, Multi-terabyte Text Collection, PVLDB 2010, Unprecedented Rate