In today’s digital world, counter-terrorism is considered as one of the highest priorities in defense departments worldwide. Organizations are investing in the development of new tools that harness advanced information technology to detect and counter terrorism through in-depth analysis of online data, especially online social networks (OSNs). A militant terrorist groups are exploiting these networks in the aim to promote their organizations and recruit more naive people inside their dangerous communities, the most used social network by these groups is Twitter. However, the existing approaches are not very efficient or they do not study the behaviors of these malicious users and they only rely on textual data provided by the users. In this paper, we propose a novel computational model using various machine learning and recommender systems techniques for detecting and predicting the influence of terrorists’ behaviors on social networks from their text-posted and image-posted content, as well as building social graphs of terrorist networks that are helpful for any further social network analysis.