Computational methods for counterterrorism

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Modern terrorist networks present a significant threat to international security, complicated by their fluid structures, ideological motivations, and non-territorial goals. Governments and militaries are developing new policies to combat this threat but require advanced technologies for effective implementation. This book compiles current computational research addressing key issues in countering terrorism, such as finding and evaluating relevant information from vast data stores, simulating and predicting enemy actions, and generating actionable intelligence by uncovering patterns in noisy data. The content is organized into four sections: discovering relevant information within unstructured data; extracting meaningful insights from multilingual digitized documents; analyzing graphs and networks to understand adversaries' intentions; and creating software systems that allow analysts to model and predict real-world conflict effects. The research presented is essential for governmental decision-makers crafting counter-terrorism policies, military and intelligence personnel developing strategies, and researchers seeking to improve knowledge discovery methods in complex datasets.