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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.

Since the inception of the World Wide Web, security agencies, researchers, and analysts have focused much of their attention on the sentiment found on hate-inspired web-forums. Here, one of their goals has been to detect and measure users’ affects that are expressed in the forums as well as identify how users’ affects change over time. Manual inspection has been one way to do this; however, as the number of discussion posts and sub-forums increase, there has been a growing need for an automated system that can assist humans in their analysis. The aim of this paper, then, is to detect and measure a number of affects expressed in written text on Stormfront.org, the most visited hate forum on the Web. To do this, we used a machine learning approach where we trained a model to recognize affects on three sub-forums: Ideology and Philosophy, For Stormfront Ladies Only, and Stormfront Ireland. The training data consisted of manual annotated data and the affects we focused on were racism, aggression, and worries. Results indicate that even though measuring affects is a subjective process, machine learning is a promising way forward to analyze and measure the presence of different affects on hate forums.

This article presents a thematic network analysis of Dabiq—a prominent English-language e-magazine produced by the Islamic State. Through formal qualitative analysis, the article examines the e-magazine’s first 13 issues in order to better understand its structure, evolution and intended audiences. In terms of structure, thematic network analysis provides a comprehensive and holistic understanding of Dabiq’s themes, identifying a range of concerns that are broader and more complex than is often supposed by academic and professional commentators. In terms of evolution, this analysis reveals a thematic landscape that has demonstrated considerable dynamism over four distinct phases throughout the magazine’s publication. In terms of understanding audiences, it is argued that Dabiq has been particularly engaged with the manipulation of group-level identities in an apparent attempt to garner support from global audiences. Themes related to allegiance, the group’s strengths and victories, and territorial expansion all feature consistently and prominently. They seek to create an in-group identity centred on victory, and to frame the Islamic State’s expansion and successes as a group achievement on behalf of Islam itself. Additionally, Dabiq provides the Islamic State with an opportunity to justify its actions and its religious authenticity to a broader Muslim audience, and thus provide the Islamic State with legitimacy beyond its borders. Recognising these thematic dynamics will be important for those engaged in counter-messaging and the development of counternarratives.

This paper, which takes the form of a case study, aims to contribute to the debate on activities of the Islamic State’s unofficial media bureaus. Based on tools of open source intelligence, as well as a limited content analysis, it maps the online presence and activities of Ahlut-Tawhid Publications (AHP). Its means of distributing pro-Daesh content in the surface web as well as its general impact are discussed. It also deliberates on the interconnectedness of AHP with other online propaganda cells supporting the self-proclaimed “Caliphate.” This paper argues that this group was part of the ongoing online campaign of the Islamic State in the World Wide Web in 2018 and 2019. It maintained quite an impressive and long-lasting online presence, combining the potential of the most popular microblogs, hosting services and social media with the flexibility of standalone websites. In contrast to the most recognized propaganda cells of Daesh, such as al-Hayat Media Centre or Amaq News Agency whose productions have been quickly detected and removed from the mainstream webpages for years, AHP kept a low profile for the most part of 2018. In effect, it benefited from its relative anonymity and for months operated a network of pro-IS distribution channels throughout Web 1.0 and Web 2.0 environments. This ceased to be the case in 2019, when most of them were incapacitated (banned) by law enforcement or abandoned. It is clear that the attention given to proliferating propaganda through the surface web decreased at this time, probably in favor of the Telegram communication software, as the discovered statistics suggest. The only active (still updated) locations—partially related to Ahlut-Tawhid Publications—belonged to the Bengali Ansar network. It has to be stressed, however, that AHP failed to spark increased attention of Internet users.

Online disruption efforts generally aim to reduce the availability of jihadist content. Yet, the speed and agility of jihadist movements online – a multiplatform approach which a co-author of this paper has previously described as a ‘swarmcast’ – has allowed groups to evolve in response to disruption efforts and find new ways to distribute content. This paper describes a model of the flow of users between social media platforms and surface web pages to access jihadist content, using data obtained through innovative collection methods. The model provides an approximate picture of the jihadist information ecosystem and how multiple platforms are used to disseminate content.