Analysing users’ behaviour and social activity for investigating suspects is an area of great interest nowadays, particularly investigating the activities of users on Online Social Networks (OSNs) for crimes. The criminal activity analysis provides a useful source of information for law enforcement and intelligence agencies across the globe. Current approaches dealing with the social criminal activity analysis mainly rely on the contextual analysis of data using only co-occurrence of terms appearing in a document to find the relationship between criminal activities in a network. In this paper, we propose a model for automated social network analysis in order to assist law enforcement and intelligence agencies to predict whether a user is a possible suspect or not. The model uses web crawlers suited to retrieve users’ data such as posts, feeds, comments, etc., and exploits them semantically and contextually using an ontology enhancement objective metric SEMCON. The output of the model is a probability value of a user being a suspect which is computed by finding the similarity between the terms obtained from the SEMCON and the concepts of criminal ontology. An experiment on analysing the public information of 20 Facebook users is conducted to evaluate the proposed model.