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Without measures of the lethality of terrorist networks, it is very difficult to assess if capturing or killing a terrorist is effective. We present the predictive lethality analysis of terrorist organization () algorithm, which merges machine learning with techniques from graph theory and social network analysis to predict the number of attacks that a terrorist network will carry out based on a network structure alone. We show that is highly accurate on two novel datasets, which cover Al Qaeda (AQ) and the Islamic State (ISIS). Using both machine learning and statistical methods, we show that the most significant macrofeatures for predicting AQ’s lethality are related to their public communications (PCs) and logistical subnetworks, while the leadership and operational subnetworks are most impactful for predicting ISISs lethality. Across both groups, the average degree and the diameters of the strongly connected components (SCCs) within these networks are strongly linked with lethality.

As a growing number of women engage in violent extremism, urgent questions about their recruitment and motivations are yet to be answered, particularly on the role of social media. Extremist organizations such as the Islamic State are adept at using social media messages to attract Western followers. Less clear is what tools can be used to deter recruitment when female extremists are taking a bigger part in orchestrating these campaigns. Join the Conflict Prevention and Resolution Forum at the U.S. Institute of Peace on May 10 for a discussion of women, social media and extremism.

In the view of many analysts, coercion is the reason most women join violent extremist groups, insurgencies and revolutionary organizations. There are, however, more sophisticated, nuanced and complex explanations such as a search for identity and sense of belonging. At the forum, a panel of experts will consider these motives and the means to address them online in the context of countering violent extremism. Join the conversation on Twitter with #CPRF.

This article examines the discursive practices of Facebook users who use the platform to express racist views. We analyzed 51,991 public comments posted to 119 news stories about race, racism, or ethnicity on the Canadian Broadcasting Corporation News Facebook page. We examined whether users who hold racist viewpoints (the vocal minority) are less likely to express views that go against the majority view for fear of social isolation. According to the “spiral of silence” theory, the vocal minority would presumably fear this isolation effect. However, our analysis shows that on Facebook, a predominantly nonanonymous and moderated platform, the vocal minority are comfortable expressing unpopular views, questioning the explanatory power of this popular theory in the online context. Based on automated analysis of 8,636 comments, we found 64 percent mentioned race or ethnicity, and 18 percent exhibited some form of othering. A manual coding of 1,161 comments showed that 18 percent exhibited some form of othering, and 25 percent countered the racist discourse. In sum, while Facebook provides space to express racist discourse, users also turn to this platform to counter the hateful narratives.

Blogs, often treated as the equivalence of online personal diaries, have become one of the fastest growing types of Web-based media. Everyone is free to express their opinions and emotions very easily through blogs. In the blogosphere, many communities have emerged, which include hate groups and racists that are trying to share their ideology, express their views, or recruit new group members. It is important to analyse these virtual communities, defined based on membership and subscription linkages, in order to monitor for activities that are potentially harmful to society. While many Web mining and network analysis techniques have been used to analyze the content and structure of the Web sites of hate groups on the Internet, these techniques have not been applied to the study of hate groups in blogs. To address this issue, we have proposed a semi-automated approach in this research. The proposed approach consists of four modules, namely blog spider, information extraction, network analysis, and visualization. We applied this approach to identify and analyze a selected set of 28 anti-Blacks hate groups (820 bloggers) on Xanga, one of the most popular blog hosting sites. Our analysis results revealed some interesting demographical and topological characteristics in these groups, and identified at least two large communities on top of the smaller ones. The study also demonstrated the feasibility in applying the proposed approach in the study of hate groups and other related communities in blogs.

Islamic State (IS) terrorist networks in Syria and Iraq pose threats to national security. IS’ exploitation of social media and digital strategy plays a key role in its global dissemination of propaganda, radicalization, and recruitment. However, systematic research on Islamic terrorist communication via social media is limited. Our research investigates the question: How do IS members/supporters use Twitter for terrorism communication: propaganda, radicalization, and recruitment? Theoretically, we drew on microeconomic network theories to develop a theoretical framework for multi-sided Twitter networks in the global Islamic terrorist communication environment. Empirically, we collected 3,039 tweets posted by @shamiwitness who was identified in prior research as “an information disseminator” for the IS cause. Methodologically, we performed social network analysis, trend and content analyses of the tweet data. We find strong evidence for Shamiwitness-intermediated multi-sided Twitter networks of international mass media, regional Arabic mass media, IS fighters, and IS sympathizers, supporting the framework’s utility.