Analysing social media as a hybrid tool to detect and interpret likely radical behavioural traits for national security. The study of National Security and its  :7,is a sensitive and complex paradigm. It encapsulates both the protection of the territorial integrity  of a :23,state, as well as guaranteeing the  its :32,population. Known as Human Security,  :39,arising from radical activities need to be mitigated  :48,may escalate and have implications on National  :56,modern era has introduced further disruptive  as :63,Hybrid Threats, that use nontraditional tools  to :71,intensify the impact of a likely threat. Social  a :81,clear illustration of such tools, where the stability of  and its :92,people can be compromised by the  :101,material. The ability to identify behaviour bordering  :109,within the deregulated world of Social Media :7,is  Security :118,imperative for governments. This paper  from :125,our earlier work to detect affected National  :134,through the analysis of social media  :141,trigger an alert when a likely threat :7,is detected. As a  :153,set of crisis interpretation processes are started to construe  such as radical behaviour  paper :168,details the methodological approach to analyse  :176,Tool (Social Media) in order to identify likely  :185,based on the Human Security spectrum and  detect and :193,interpret dissimilar behavioural patterns  radical behavioural :200,traits for National Security.  :207,methodology focuses on five steps, namely  :214,Entity Extraction, Wordlists Creation, Content Analytics, and Data Interpretation.

Rule Based Method for Terrorism, Violence and Threat Classification: Application to Arabic Tweets In this paper, we present a rule based method to classify Tweets under three main categories; terrorism, violence and threat classes. Given that Arabic is a morphologically complex language, we build a linguistic module to identify a set of patterns for each class. Our proposed method requires three fundamental steps: First, we create our reference corpus collected from Arabic tweets. From the study of this corpus, we identify a set of linguistic rules. Finally, these patterns will be rewritten into local grammar within the linguistic platform NooJ. The evaluation of our system achieved encouraging results to obtaining 84%, 86.8% and 84.7% in terms of recall, precision and fscore respectively, when applied to test corpus.