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Predicting terrorist attacks in the United States using localized news data.

Steven J Krieg1, Christian W Smith2, Rusha Chatterjee2

  • 1Lucy Family Institute for Data and Society, University of Notre Dame, Notre Dame, IN, United States of America.

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Summary

Machine learning models can predict terrorist attacks using localized news data. A Random Forest model achieved significant accuracy, demonstrating the value of treating attacks as independent events for improved counter-terrorism efforts.

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Area of Science:

  • Computational Social Science
  • Artificial Intelligence
  • National Security

Background:

  • Terrorism poses a significant global threat, causing numerous fatalities and substantial economic damage annually.
  • Existing approaches to terrorism prediction often struggle with sparse and dissimilar historical event data.
  • The need for effective predictive models to mitigate terrorism risks is paramount.

Purpose of the Study:

  • To develop and evaluate a novel feature representation method for predicting terrorist attacks.
  • To assess the efficacy of machine learning models trained on localized news data for terrorism prediction.
  • To determine if treating terrorism as independent events improves predictive accuracy.

Main Methods:

  • Proposed a novel feature representation method for time-series data.
  • Evaluated machine learning models, including a Random Forest with a variable-length moving average.
  • Trained and tested models on localized news data for specific states in the United States.
  • Utilized the area under the receiver operating characteristic (AUROC) curve to measure model performance.

Main Results:

  • The best performing model, a Random Forest, achieved an AUROC of ≥ 0.667 (p ≤ .0001) in four of the five most-impacted states.
  • The model's success indicates that localized news data contains valuable predictive information.
  • Treating terrorism as a set of independent events proved more effective than a continuous process model.
  • Prediction success was not correlated with specific attack characteristics like group or weapon type.

Conclusions:

  • Localized machine learning models trained on news data can effectively predict terrorist attacks.
  • The approach of modeling terrorism as independent events is beneficial, particularly with limited historical data.
  • Future counter-terrorism strategies can leverage these findings for enhanced threat assessment and prevention.
  • The study provides a foundation for applying machine learning to national security challenges.