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Summary Since the appearance of social networks, there was a historic increase of data. Unfortunately, terrorists are taking advantage of the easiness of accessing social networks and they have set up profiles to recruit, radicalize, and raise funds. Most of these profiles have pages that exist as well as new recruits to join the terrorist groups, see, and share information. Therefore, there is a potential need for detecting terrorist communities in social networks in order to search for key hints in posts that appear to promote the militants' cause. In order to remedy this problem, we first use a possibilistic‐clustering algorithm that allows more flexibility when assigning a social network profile to clusters (non‐terrorist, terrorist‐sympathizer, terrorist). Then, we introduce a new possibilistic flexible graph mining method to discover similar subgraphs by applying possibilistic similarity rather than using hard structural exact similarity. We experimentally show the efficiency of our possibilistic approach through a detailed process of tweets extract, semantic processing, and classification of the community detection.

Common stream mining tasks include classification, clustering and frequent pattern mining among them; data stream classification has drawn particular attention due to its vast real-time application. Through these applications, the main goal is to efficiently build classification models from data streams for accurate prediction. The development of such model has shown the need for machine learning techniques to be applied to large scale data. A range of machine learning techniques exists and the selection of the accurate techniques is based on advantages and limits of each one and how these latter well addresses important research techniques. In this paper, we present the comparison of different classification techniques using WEKA in order to investigate the performance of a collection of classification algorithms. This comparison shows the support vector machine performance with higher accuracy and better results when classifying our dataset.

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