@inbook {807,
	title = {Safe Travelling Period Recommendation to High Attack Risk European Destinations Based on Past Attack Information},
	booktitle = {Lecture Notes in Social Networks},
	year = {2021},
	pages = {77{\textendash}100},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {<p>Terrorism is a significant deterrent for tourism. It affects both visitors and local citizens and personnel of a country or area. On one hand, the potential visitor will probably avoid travelling to a high attack risk country, due to safety reasons, hence will miss the opportunity to visit it, and, on the other hand, the country{\textquoteright}s tourism will decline. This work addresses the aforementioned problem by (1) showing that relatively safe visiting periods for high attack risk European countries can be predicted with high accuracy, using limited information, comprising of attack and fatality data from the past years, which are widely available, and (2) developing an algorithm that recommends relatively safe periods to potential travellers. The results of this work will be useful for tourists, visitors, businesses and operators, as well as relevant stakeholders and actors.</p>
},
	keywords = {Evaluation, Recommendation algorithm, Risk Calculation, Safety Perception, Safety prediction, Terrorist Attacks, tourism},
	doi = {10.1007/978-3-030-67044-3_3},
	author = {Dimitris Spiliotopoulos and Dionisis Margaris and Costas Vassilakis}
}
@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}
}
