Recent terrorist attacks carried out on behalf of ISIS on American and European soil by lone wolf attackers or sleeper cells remind us of the importance of understanding the dynamics of radicalization mediated by social media communication channels. In this paper, we shed light on the social media activity of a group of twenty-five thousand users whose association with ISIS online radical propaganda has been manually verified. By using a computational tool known as dynamical activity-connectivity maps, based on network and temporal activity patterns, we investigate the dynamics of social influence within ISIS supporters. We finally quantify the effectiveness of ISIS propaganda by determining the adoption of extremist content in the general population and draw a parallel between radical propaganda and epidemics spreading, highlighting that information broadcasters and influential ISIS supporters generate highly-infectious cascades of information contagion. Our findings will help generate effective countermeasures to combat the group and other forms of online extremism.
Scanlon and Gerber proposed the first method to detect cyber-recruitment efforts in 2014 @cite_36 . They exploited data retrieved from the Dark Web Portal Project @cite_44 , a repository of posts compiled from 28 different online fora on extremist religious discussions (e.g., Jihadist) translated from Arabic to English. After annotating a sample of posts as recruitment efforts or not, the authors use Bayesian criteria and a set of textual features to classify the rest of the corpus, obtaining good accuracy, and highlighted the most predictive terms.
@cite_36 @cite_44
2168359436 2101287943
Growing use of the Internet as a major means of communication has led to the formation of cyber-communities, which have become increasingly appealing to terrorist groups due to the unregulated nature of Internet communication. Online communities enable violent extremists to increase recruitment by allowing them to build personal relationships with a worldwide audience capable of accessing uncensored content. This article presents methods for identifying the recruitment activities of violent groups within extremist social media websites. Specifically, these methods apply known techniques within supervised learning and natural language processing to the untested task of automatically identifying forum posts intended to recruit new violent extremist members. We used data from the western jihadist website Ansar AlJihad Network, which was compiled by the University of Arizona’s Dark Web Project. Multiple judges manually annotated a sample of these data, marking 192 randomly sampled posts as recruiting (Yes) or non-recruiting (No). We observed significant agreement between the judges’ labels; Cohen’s κ=(0.5,0.9) at p=0.01. We tested the feasibility of using naive Bayes models, logistic regression, classification trees, boosting, and support vector machines (SVM) to classify the forum posts. Evaluation with receiver operating characteristic (ROC) curves shows that our SVM classifier achieves an 89 area under the curve (AUC), a significant improvement over the 63 AUC performance achieved by our simplest naive Bayes model (Tukey’s test at p=0.05). To our knowledge, this is the first result reported on this task, and our analysis indicates that automatic detection of online terrorist recruitment is a feasible task. We also identify a number of important areas of future work including classifying non-English posts and measuring how recruitment posts and current events change membership numbers over time. In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. The emergence of the Web 2.0 technology generated a massive amount of raw data by enabling Internet users to post their opinions, reviews, comments on the web. Processing this raw data to extract useful information can be a very challenging task. An example of important information that can be automatically extracted from the users' posts and comments is their opinions on different issues, events, services, products, etc. This problem of Sentiment Analysis (SA) has been studied well on the English language and two main approaches have been devised: corpus-based and lexicon-based. This paper addresses both approaches to SA for the Arabic language. Since there is a limited number of publically available Arabic dataset and Arabic lexicons for SA, this paper starts by building a manually annotated dataset and then takes the reader through the detailed steps of building the lexicon. Experiments are conducted throughout the different stages of this process to observe the improvements gained on the accuracy of the system and compare them to corpus-based approach. In this article we investigate the evolution of ISIS by analysing the text contained in Dabiq, the official ISIS’ internet magazine in English. Specifically, we used a computerized text analysis pro-gram LIWC (Linguistic Inquiry and Word Count) to investigate the evolution of the language of the first 11 Issues of Dabiq. First, our analysis showed that affiliation seems to be an increasingly important psychological motive for the group. Secondly, ISIS has been increasingly using emotions, which are an important mobilizing factor in collective action literature, in a strategic manner. Thirdly, ISIS language presents an increasing concern with females. Last but not least, our analysis shows that ISIS has been increasingly using internet jargon (net-speak), which shows how the group tries to adapt itself to the internet environment and to connect with the identities of young individuals. ISIS is of particular concern as it seems to be more successful at recruiting foreign fighters than other jihadist groups (such as al-Qaeda). This aspect is difficult to quantify because reliable it is hard to access data about the exact number, demographics and affiliation of the foreign fighters who joined those organizations. Yet according to reports by the European Union there is a concerning increase in recruitment rates and patterns, with ISIS being more successful at recruiting young individuals from Western countries than any other jihadist group (Archick, Belkin, Blanchard, Hemud, & Mix, 2015). Scholars suggested that the majority of newly recruited jihadists prefer ISIS to other jihadist groups (Karmon, 2015; Klausen, 2015; Peresin, 2015; Peresin & Cervone, 2015; Turner, 2015)and journalists reported that existing members of al-Qaeda even abandoned the group to join ISIS (Dilanian, 2015; Miller, 2014). Moreover, a 2015 report from the International Centre for the Study of Radicalisation and Political Violence based at London King’s College stated that the number of jihadist foreign fighters in Syria