Abstract
Developments in statistics and computer science have influenced research on many social problems. This process also applies to the study of terrorism. In this context, network analysis is one of the most popular mathematical methods for analyzing terrorist organizations and dynamics. Nonetheless, few studies have applied network science to the analysis of terrorist events. Therefore, in this work we first introduce a novel method to analyze the heterogeneous dynamics of terrorist attacks through the creation of a dynamic meta-network of terror for the period 1997–2016. Second, we use our terrorist meta-network to test the power of Network-based Inference algorithm in predicting terrorist targets. Results are promising and show how this algorithm reaches high levels of precision, accuracy, and recall and indicate that network outcomes can be used in broader machine learning models.
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Notes
- 1.
Western Europe region data from 1997–2016 include Austria, Belgium, Cyprus, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Malta, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland and United Kingdom. North America region data for the same period include Canada, Mexico and United States of America.
- 2.
We used the general target type information available in the dataset to prevent problems of over-specification and noise in the data, considering that a more general description reduces the risk of coding error, consequently preserving results reliability.
- 3.
It is expectable that taking into account attacks of those groups that existed at (t−1) improves accuracy of prediction. Though not empirically confirmed in this work, future work will test this hypothesis on all pairs of years included in the analysis.
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Acknowledgments
This work was supported in part by the Office of Naval Research (ONR) Multidisciplinary University Research Initiative Award N00014-17-1-2675 and the Center for Computational Analysis of Social and Organization Systems (CASOS). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the ONR or the U.S. government.
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Campedelli, G.M., Cruickshank, I., Carley, K.M. (2018). Complex Networks for Terrorist Target Prediction. In: Thomson, R., Dancy, C., Hyder, A., Bisgin, H. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2018. Lecture Notes in Computer Science(), vol 10899. Springer, Cham. https://doi.org/10.1007/978-3-319-93372-6_38
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