Detecting Terrorism Incidence Type from News Summary

Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 126)

Abstract

The paper presents the experiments to detect terrorism incidence type from news summary data. We have applied classification techniques on news summary data to analyze the incidence and detect the type of incidence. A number of experiments are conducted using various classification algorithms and results show that a simple decision tree classifier can learn incidence type with satisfactory results from news data.

Keywords

GTD Classification Decision tree Naïve Bayes SVM 

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References

  1. 1.
  2. 2.
    Laura, D., Gary, L., Piquero, A.R.: Testing a Rational Choice Model of Airline Hijackings. Criminology 43, 1031–1065 (2005)CrossRefGoogle Scholar
  3. 3.
    Robert, G., Laura, D., LaFree, G.: The Impact of Terrorism on Italian Employment and Business Activity. Urban Studies 44, 1093–1108 (2007)CrossRefGoogle Scholar
  4. 4.
    Gary, L., Laura, D.: Tracking Global Terrorism, 1970-2004. In: Weisburd, D., Feucht, T., Hakimi, I., Mock, L., Perry, S. (eds.) To Protect and to Serve: Police and Policing in an Age of Terrorism. Springer, New York (2009)Google Scholar
  5. 5.
    Gary, L., Laura, D., Korte, R.: The Impact of British Counter Terrorist Strategies on Political Violence in Northern Ireland: Comparing Deterrence and Backlash Models. Criminology 47, 501–530 (2009)Google Scholar
  6. 6.
    Gary, L., Yang, S.-M., Crenshaw, M.: Trajectories of Terrorism: Attack Patterns of Foreign Groups that have targeted the United States, 1970 to 2004. Criminology and Public Policy 8, 445–473 (2009)CrossRefGoogle Scholar
  7. 7.
    Guo, D., Liao, K., Morgan, M.: Environment and Planning B: Planning and Design. Visualizing patterns in a global terrorism incident database 34, 767–784 (2007)Google Scholar
  8. 8.
    Wu, X., Kumar, V., Quinlan, J.R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Yu, P.S., Zhou, Z.H., Steinbach, M., Hand, D.J., Steinberg, D.: Top 10 algorithms in data mining, Survey paper. Springer, Heidelberg (2007)Google Scholar
  9. 9.
    Quinlan, J.R.: Induction of decision trees. Journal of Machine Learning 1, 81–106 (1986)Google Scholar
  10. 10.
    Quinlan, J.R.: C4.5: Programs for machine learning. Machine Learning, vol. 16, pp. 235–240. Springer, Heidelberg (1993)Google Scholar
  11. 11.
    McCallum, D.J.: Nigam. K.: A Comparison of event models for Naive Bayes text classification. Technical Report. Workshop on learning for text categorization. pp. 41–48 (1998)Google Scholar
  12. 12.
    Joachims, T.: A statistical learning model of text classification for Support Vector Machines. In: International ACM SIGIR Conference on Research and Development in Information Retrieval (2001)Google Scholar
  13. 13.
    Vapnik, V.: The nature of statistical theory. Springer, Heidelberg (1995)MATHGoogle Scholar
  14. 14.
    Hall, M., Frank, E., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data mining software: An Update. SIGKDD Explorations 11(1) (2009)Google Scholar
  15. 15.
    Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34(1), 1–47 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Maersk McKinney Moller InstituteUniversity of SouthernOdenseDenmark
  2. 2.University of SindhJamshoroPakistan

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