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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.

The Online Social Network (OSN) has evolved as a popular platform enabling rich topic-centric interactions and serving as a medium to facilitate online radicalization (Behr et al. 2013). Keeping in view the growing need of uncovering online radicalization, we focus on the information network of Twitter and present an approach for identifying dynamic communities, which arise due to “radical” topic-centric user interactions. The approach at successive timestamps deploys evolving topic-entity maps along with evolving interaction graphs. We propose “Rate of Overlap (ROAct)” to determine the similarity among successive community timestamps. We further validate our approach using an open dataset of criminal offences in the city of Denver, Colorado. The approach presented is simple, fast, and effective for dynamic identification of topic-centric communities and, thus, will enable law enforcement agencies to identify hidden radicalization.

Online video sharing platforms such as YouTube contains several videos and users promoting hate and extremism. Due to low barrier to publication and anonymity, YouTube is misused as a platform by some users and communities to post negative videos disseminating hatred against a particular religion, country or person. We formulate the problem of identification of such malicious videos as a search problem and present a focused-crawler based approach consisting of various components performing several tasks: search strategy or algorithm, node similarity computation metric, learning from exemplary profiles serving as training data, stopping criterion, node classifier and queue manager. We implement two versions of the focused crawler: best-first search and shark search. We conduct a series of experiments by varying the seed, number of n-grams in the language model based comparer, similarity threshold for the classifier and present the results of the experiments using standard Information Retrieval metrics such as precision, recall and F-measure. The accuracy of the proposed solution on the sample dataset is 69% and 74% for the best-first and shark search respectively. We perform characterization study (by manual and visual inspection) of the anti-India hate and extremism promoting videos retrieved by the focused crawler based on terms present in the title of the videos, YouTube category, average length of videos, content focus and target audience. We present the result of applying Social Network Analysis based measures to extract communities and identify core and influential users.

The growing penetration of online social networks (OSNs) has emerged as a strong operational tool for spreading online radicalization by adding intensity and acceleration to the operations of radical groups. The efficiency of ways and methods proposed to measure the dynamics of radicalization are limited by the availability of models for modeling the intricacies of human dynamics and heterogeneous time varying interactions. Most of the developed algorithms and tools mainly focus on the static network structures and tend to ignore the “origin” of a new radical idea or propagation of a “weak” topic which might find its relevance in the security domain later on with the passage of time. In this article we propose a mathematical model for measuring dynamic propagation of radicalization in OSNs using Discrete Time Markov Chains by discretizing the continuous time domain. We also discuss our experimental results obtained through the information network of Twitter. The approach presented here is scalable and can easily be extended for modeling diffusion of general topics in OSNs.

The rise of ISIS and associated jihadi violence taking place in Syria and Iraq has reverberated widely. The effects can be felt not just in the horrific attacks that took place in Paris in January 2015, but across the Asia-Pacific region as well, including Australia. Public officials and analysts are struggling to understand and devise countermeasures to the recruitment mechanisms employed by ISIS and other violent extremist groups. This essay explores the role that social media has played in ISIS’s efforts to attract adherents.

This article considers claims made by various authors that the use of filtering and recommendation technology on the Internet can deprive certain communities of feedback, and instead amplify groups’ viewpoints, leading to polarization of opinion across communities, and increases in extremism. The ‘echo chamber’ arguments of Cass Sunstein are taken as representative of this point of view, and examined in detail in the context of a range of research, theoretical and empirical, quantitative and qualitative, in political science and the sociology of religion, from the last quarter century. The conclusion is that the case has not been made either (a) that echo chambers are necessarily harmful, or (b) that the Internet is complicit in their formation.