A Fuzzy-GA for Predicting Terrorist Networks in Social Media

Abstract

Global terrorist activities increase with the evolution of various social media sites such as Facebook, Twitter, etc. Various organizations use a wide scope of network capabilities of social media to broadcast their information, propaganda, as well communicate their strategic objectives. So, by analyzing such growing terrorist activity over online social media using mining and analysis, various valuable insights can be predicted. This chapter approaches an effective way of analyzing such activities by identifying nearest nodes in the network. The terrorist network mining algorithm has assisted by successfully achieving terrorist activities and their behavior on nodes of social network using centrality algorithm. The algorithm works in two phases: 1) fuzzification of data to measure centrality between nodes in the network and 2) applying genetic approach for the optimization of data and to increase the searching capability for appropriate cluster centers.