Rapid developments in information technology and Web 2.0 have provided a platform for the evolution of terrorist organizations, extremists from a traditional pyramidal structure to a technology enabled networked structure. Growing presence of these subversive groups on social networking sites has emerged as one of the prominent threats to the society, governments and law enforcement agencies across the world. Identifying messages relevant to the domain of security can serve as a stepping stone in criminal network analysis. In this paper, we deploy a rule based approach for classifying messages in Twitter which can also successfully reveal overlapping clusters. The approach incorporates dictionaries of enriched themes where each theme is categorized by semantically related words. The message is vectorized according to the security dictionaries and is termed as ‘Security Vector’. The documents are classified in categories on the basis of security associations. Further, the approach can also be used along the temporal dimension for classifying messages into topics and rank the most prominent topics of conversation at a particular instance of time. We further employ social network analysis techniques to visualize the hidden network at a particular time. Some of the results of our approach obtained through experiment with information network of Twitter are also discussed.