SaferCity: a System for Detecting Incidents from Social Media

This paper presents a system to identify and characterise public safety related incidents from social media, and enrich the situational awareness that law enforcement entities have on potentially-unreported activities happening in a city. The system is based on a new spatio-temporal clustering algorithm that is able to identify and characterize relevant incidents given even a small number of social media reports. We present a web-based application exposing the features of the system, and demonstrate its usefulness in detecting, from Twitter, public safety related incidents occurred in New York City during the Occupy Wall Street protests.

Published in:
Proceedings of the 13th IEEE International Conference on Data Mining Workshops
Presented at:
IEEE International Conference on Data Mining, Dallas, Texas, USA, December 7-10, 2013

 Record created 2013-10-10, last modified 2018-01-28

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