Predicting crime using Twitter and kernel density estimation

Predicting crime using Twitter and kernel density estimation 1 Introduction</h5> Twitter currently serves approximately 140 million worldwide users posting a combined 340 million messages (or tweets) per day [1] . Within the United States in 2012, 15% of online adults used the Twitter service and 8% did so on a typical day, with the latter number quadrupling since late 2010 [2] . The service's extensive use, both in the United States as well as globally, creates many opportunities to augment decision support systems with Twitter-driven predictive analytics. Recent research has shown that tweets can be used to predict various large-scale events like elections [3] , infectious disease outbreaks [4] , and national revolutions [5] . The essential hypothesis is that the location, timing, and content of tweets are informative with regard to future events.</P>Motivated by these prior studies, this article presents research answering the following question: can we use the tweets posted by residents in a major U.S. city to predict local criminal activity? This is an important question because tweets are public information and they are easy to obtain via the official Twitter service. Combined with Twitter's widespread use around the globe, an affirmative answer to this question could have implications for a large population http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Decision Support Systems Elsevier

Predicting crime using Twitter and kernel density estimation

Decision Support Systems, Volume 61 – May 1, 2014

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Publisher
Elsevier
Copyright
Copyright © 2014 Elsevier B.V.
ISSN
0167-9236
eISSN
1873-5797
DOI
10.1016/j.dss.2014.02.003
Publisher site
See Article on Publisher Site

Abstract

1 Introduction</h5> Twitter currently serves approximately 140 million worldwide users posting a combined 340 million messages (or tweets) per day [1] . Within the United States in 2012, 15% of online adults used the Twitter service and 8% did so on a typical day, with the latter number quadrupling since late 2010 [2] . The service's extensive use, both in the United States as well as globally, creates many opportunities to augment decision support systems with Twitter-driven predictive analytics. Recent research has shown that tweets can be used to predict various large-scale events like elections [3] , infectious disease outbreaks [4] , and national revolutions [5] . The essential hypothesis is that the location, timing, and content of tweets are informative with regard to future events.</P>Motivated by these prior studies, this article presents research answering the following question: can we use the tweets posted by residents in a major U.S. city to predict local criminal activity? This is an important question because tweets are public information and they are easy to obtain via the official Twitter service. Combined with Twitter's widespread use around the globe, an affirmative answer to this question could have implications for a large population

Journal

Decision Support SystemsElsevier

Published: May 1, 2014

References

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