Key Player Identification in Terrorism-Related Social Media Networks Using Centrality Measures

Monitoring terrorist groups and their suspicious activities in social media is a challenging task, given the large amounts of data involved and the need to identify the most influential users in a smart way. To this end, many efforts have focused on using centrality measures for the identification of the key players in terrorism-related social media networks, so that their suspension/removal leads to severe disruption in the connectivity of the network. This work proposes a novel centrality measure, Mapping Entropy Betweenness (MEB), and assesses its effectiveness for key player identification on a dataset of terrorism-related Twitter user accounts by simulating targeted attacks that remove the most central nodes of the network. The results indicate that the MEB affects the robustness of this terrorist network more than well-established centrality measures.