By Topic

Machine learning for the automatic identification of terrorist incidents in worldwide news media

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Mason, R. ; RAND Corp., Santa Monica, CA, USA ; McInnis, B. ; Dalal, S.

The RAND Database of Worldwide Terrorism Incidents (RDWTI) seeks to index information about all terrorist incidents that occur and are mentioned in worldwide news media, providing a useful resource for policy researchers and decision makers. We examined automated classification methods that could be used to identify news articles about terrorist incidents, thus enabling analysts to read a smaller number of news articles and maintain the database with less effort and cost. The support vector machine (SVM) and Lasso methods were only modestly successful, but a classifier based on the gradient boosting method (GBM) appeared to be very successful, correctly ranking 80% of the relevant articles at the “top of the pile” for examination by a human analyst.

Published in:

Intelligence and Security Informatics (ISI), 2012 IEEE International Conference on

Date of Conference:

11-14 June 2012