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Twitter Crossfire : Terror Attack Detection via Probabilistic Classifiers
Herman Wandabwa,Liao Zhifang,Korir Sammy 보안공학연구지원센터 2015 International Journal of Database Theory and Appli Vol.8 No.4
The advent of social computing brought with it different social networking platforms. The idea of surfers socializing with people of different backgrounds as well as geographical regions is quite fascinating. In our approach, we delved deeper in disaster discovery whereby we extracted panic related attributes and trained them with real data in three disaster scenarios in different parts of the world. Fine tuning of the final attributes led to accuracies above 91% proving the fact that with proper attribute selection and handling of sparse data balance, it’s possible to detect related disasters as soon as related tweets appear. We believe that we are the first to use probabilistic classifiers approach as well as NLP in specifically human induced terror attacks detection as there is no known system currently that solely caters for these.