@conference {spiliotopoulos_2019_asonam,
	title = {Data-driven Country Safety Monitoring Terrorist Attack Prediction},
	booktitle = {Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019)},
	year = {2019},
	month = {aug},
	abstract = {Terrorism is a key risk for prospective visitors of tourist desti-nations. This work reports on the analysis of past terrorist at-tack data, focusing on tourist-related attacks and attack types in Mediterranean EU area and the development of algorithms to predict terrorist attack risk levels. Data on attacks in 10 coun-tries have been analyzed to quantify the threat level of tourism-related terrorism based on the data from 2000 to 2017 and for-mulate predictions for subsequent periods. Results show that predictions on potential target types can be derived with ade-quate accuracy. Such results are useful for initiating, shifting and validating active terrorism surveillance based on real pre-dicted attack and target types per country.},
	keywords = {Data Analysis, Risk Calculation, Safety Perception, Terrorist Attacks, Threat Ranking},
	author = {Dimitris Spiliotopoulos and Costas Vassilakis and Dionisis Margaris}
}
