Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICDMW.2013.39 | ICDM Workshops |
Keywords | Field | DocType |
new spatio-temporal,law enforcement entity,new york city,analyzing incidents,occupy wall street protest,social media,social media report,characterise public safety,relevant incident,potentially-unreported activity,public safety,law,internet | Data mining,Internet privacy,Social media,Social media optimization,Computer science,Situation awareness,Law enforcement,Cluster analysis,Semantics,The Internet | Conference |
ISSN | ISBN | Citations |
2375-9232 | 978-1-4799-3143-9 | 6 |
PageRank | References | Authors |
0.54 | 1 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michele Berlingerio | 1 | 510 | 28.92 |
| Francesco Calabrese | 2 | 242 | 15.93 |
| Giusy Di Lorenzo | 3 | 574 | 34.54 |
| Xiaowen Dong | 4 | 249 | 22.07 |
| Yiannis Gkoufas | 5 | 17 | 5.96 |
| Dimitrios Mavroeidis | 6 | 130 | 9.50 |
| Di Lorenzo, G. | 7 | 64 | 4.32 |