Solutions to Detect and Analyze Online Radicalization : A Survey

Online Radicalization (also called Cyber-Terrorism or Extremism or Cyber-Racism or Cyber- Hate) is widespread and has become a major and growing concern to the society, governments and law enforcement agencies around the world. Research shows that various platforms on the Internet (low barrier to publish content, allows anonymity, provides exposure to millions of users and a potential of a very quick and widespread diffusion of message) such as YouTube (a popular video sharing website), Twitter (an online micro-blogging service), Facebook (a popular social networking website), online discussion forums and blogosphere are being misused for malicious intent. Such platforms are being used to form hate groups, racist communities, spread extremist agenda, incite anger or violence, promote radicalization, recruit members and create virtual organi- zations and communities. Automatic detection of online radicalization is a technically challenging problem because of the vast amount of the data, unstructured and noisy user-generated content, dynamically changing content and adversary behavior. There are several solutions proposed in the literature aiming to combat and counter cyber-hate and cyber-extremism. In this survey, we review solutions to detect and analyze online radicalization. We review 40 papers published at 12 venues from June 2003 to November 2011. We present a novel classification scheme to classify these papers. We analyze these techniques, perform trend analysis, discuss limitations of existing techniques and find out research gaps

Semantic feature reduction and hybrid feature selection for clustering of Arabic Web pages

In the literature, high-dimensional data reduces the efficiency of clustering algorithms. Clustering the Arabic text is challenging because semantics of the text involves deep semantic processing. To overcome the problems, the feature selection and reduction methods have become essential to select and identify the appropriate features in reducing high-dimensional space. There is a need to develop a suitable design for feature selection and reduction methods that would result in a more relevant, meaningful and reduced representation of the Arabic texts to ease the clustering process. The research developed three different methods for analyzing the features of the Arabic Web text. The first method is based on hybrid feature selection that selects the informative term representation within the Arabic Web pages. It incorporates three different feature selection methods known as Chi-square, Mutual Information and Term Frequency–Inverse Document Frequency to build a hybrid model. The second method is a latent document vectorization method used to represent the documents as the probability distribution in the vector space. It overcomes the problems of high-dimension by reducing the dimensional space. To extract the best features, two document vectorizer methods have been implemented, known as the Bayesian vectorizer and semantic vectorizer. The third method is an Arabic semantic feature analysis used to improve the capability of the Arabic Web analysis. It ensures a good design for the clustering method to optimize clustering ability when analysing these Web pages. This is done by overcoming the problems of term representation, semantic modeling and dimensional reduction. Different experiments were carried out with k-means clustering on two different data sets. The methods provided solutions to reduce high-dimensional data and identify the semantic features shared between similar Arabic Web pages that are grouped together in one cluster. These pages were clustered according to the semantic similarities between them whereby they have a small Davies–Bouldin index and high accuracy. This study contributed to research in clustering algorithm by developing three methods to identify the most relevant features of the Arabic Web pages

On the Topology of the Dark Web of Terrorist Groups

Abstract. In recent years, terrorist groups have used the WWW to spread their ideologies, disseminate propaganda, and recruit members. Studying the terrorist websites may help us understand the characteristics of these websites and predict terrorist activities. In this paper, we propose to apply network topological analysis methods on systematically collected the terrorist website data and to study the structural characteristics at the Web page level. We conducted a case study using the methods on three collections of Middle-Eastern, US domestic, and Latin-American terrorist websites. We found that these three networks have the small-world and scale-free characteristics. We also found that smaller size websites which share same interests tend to make stronger inter-website linkage relationships.