Abstract
In recent decades, the world of social media became the most popular. Social media have transformed the world. The rapid and large choice of these technologies is transforming how we find communities, how we get information from the news. According to this growth of social media, cyber terrorism has become an international issue that threatens world peace. Cyberterrorism is becoming more famous on social media now. While the Internet grows more pervasive in every area interested users or organizations can use the anonymity provided by cyberspace to terrorize citizens, communities, specific groups, and entire countries, without the internal threat of capture, damage, or death to the criminal that being physically existing would begin. Besides, Social network analysis plays a key research field for detecting different groups in a cyber-terrorist network. Many researchers are interested to find these communities, the managers, and the influencers which present a predictive way to protect users of social media networks. Then, the enormous evolution of terrorist communities over time presents a big problem to analyze and detect them. In this article, we introduce a new method for communities detection according to the network of contact, the publications, and their evolution based on Twitter as a social network. Also, we find the managers and the influencers in terrorist communities using swarm techniques. Our proposed method object is to optimize our proposed objective function to have a coherent partitioning inspired by the artificial bees comportment and using the data warehouse to save data in every evolution over time. Finally, we illustrate the performance of our proposed method by an experimental study on the real and artificial network and with a comparative study with the same related recent works. We test the performance of our approach by applying different quality functions on the terrorist communities detected.