A possibilistic framework for the detection of terrorism‐related Twitter communities in social media

• Published in: Concurrency and computation 2019-07, Vol.31 (13), p.n/a
• Main Authors: Moussaoui, Mohamed, Zaghdoud, Montaceur, Akaichi, Jalel
• Format: Article
• Language: English
• Subjects: Algorithms; Clustering; Communities; community detection; Digital media; frequent subgraph mining; graph matching; Graph theory; possibilistic clustering; Similarity; Social networks; Terrorism
• ISSN: 1532-0626; 1532-0634
• DOI: 10.1002/cpe.5077

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.