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A better understanding of how support evolves online for undesirable behaviors such as extremism and hate, could help mitigate future harms. Here we show how the highly irregular growth curves of groups supporting two high-profile extremism movements, can be accurately described if we generalize existing gelation models to account for the facts that the number of potential recruits is timedependent and humans are heterogeneous. This leads to a novel generalized Burgers equation that describes these groups’ temporal evolution, and predicts a critical influx rate for potential recruits beyond which such groups will not form. Our findings offer a new approach to managing undesirable groups online – and more broadly, managing the sudden appearance and growth of large macroscopic aggregates in a complex system – by manipulating their onset and engineering their growth curves.

This article reviews the academic literature on how and for what purposes violent extremists use the Internet, at both an individual and organizational level. After defining key concepts like extremism, cyber-terrorism and online radicalization, it provides an overview of the virtual extremist landscape, tracking its evolution from static websites and password-protected forums to mainstream social media and encrypted messaging apps. The reasons why violent extremist organizations use online tools are identified and evaluated, touching on propaganda, recruitment, logistics, funding, and hacking. After this, the article turns to the ways violent extremist individuals use the Internet, discussing its role as a facilitator for socialization and learning. The review concludes by considering the emergent literature on how violent extremism is being countered online, touching on both defensive and offensive measures.

The Western Balkans faces a double challenge from online extremism. Online platforms are facilitating the specific targeting of the region by diverse international extremist narratives. Meanwhile regional histories and geopolitics are being appropriated to justify extremist actions and narratives around the world. This is part of a wider trend that underscores the growing challenge posed by the proliferation of transnational extremist ideologies on online platforms, both violent jihadist and extreme right wing. In the Western Balkans, this poses a number of specific risks. While the issue of the prevention, mitigation, and regulation of online extremism is a global one, there are a number of region-specific considerations relevant to effective policy and practitioner responses.

The Strong Cities Network (SCN) conducted an online mapping exercise to investigate the main extremist narratives deployed in North Macedonia. Using a mixed method of automated collection and expert manual qualitative online research, SCN identified the main narratives across political, ethnic and religiously inspired extremist and hate groups. On the basis of these findings, SCN provided a comprehensive set of recommendations for national government, local authorities, civil society and the private tech sector to help inform a more comprehensive response to these online harms. The findings from this study will also inform multi-agency work the network is carrying out with the community.

Social media platforms are popular for expressing personal views, emotions and beliefs. Social media platforms are influential for propagating extremist ideologies for group-building, fund-raising, and recruitment. To monitor and control the outreach of extremists on social media, detection of extremism in social media is necessary. The existing extremism detection literature on social media is limited by specific ideology, subjective validation methods, and binary or tertiary classification. A comprehensive and comparative survey of datasets, classification techniques, validation methods with online extremism detection tool is essential. The systematic literature review methodology (PRISMA) was used. Sixty-four studies on extremism research were collected, including 31 from SCOPUS, Web of Science (WoS), ACM, IEEE, and 33 thesis, technical and analytical reports using Snowballing technique. The survey highlights the role of social media in propagating online radicalization and the need for extremism detection on social media platforms. The review concludes lack of publicly available, class-balanced, and unbiased datasets for better detection and classification of social-media extremism. Lack of validation techniques to evaluate correctness and quality of custom data sets without human interventions, was found. The information retrieval unveiled that contemporary research work is prejudiced towards ISIS ideology. We investigated that deep learning based automated extremism detection techniques outperform other techniques. The review opens the research opportunities for developing an online, publicly available automated tool for extremism data collection and detection. The survey results in conceptualization of architecture for construction of multi-ideology extremism text dataset with robust data validation techniques for multiclass classification of extremism text.

Social media platforms are popular for expressing personal views, emotions and beliefs. Social media platforms are influential for propagating extremist ideologies for group-building, fund-raising, and recruitment. To monitor and control the outreach of extremists on social media, detection of extremism in social media is necessary. The existing extremism detection literature on social media is limited by specific ideology, subjective validation methods, and binary or tertiary classification. A comprehensive and comparative survey of datasets, classification techniques, validation methods with online extremism detection tool is essential. The systematic literature review methodology (PRISMA) was used. Sixty-four studies on extremism research were collected, including 31 from SCOPUS, Web of Science (WoS), ACM, IEEE, and 33 thesis, technical and analytical reports using Snowballing technique. The survey highlights the role of social media in propagating online radicalization and the need for extremism detection on social media platforms. The review concludes lack of publicly available, class-balanced, and unbiased datasets for better detection and classification of social-media extremism. Lack of validation techniques to evaluate correctness and quality of custom data sets without human interventions, was found. The information retrieval unveiled that contemporary research work is prejudiced towards ISIS ideology. We investigated that deep learning based automated extremism detection techniques outperform other techniques. The review opens the research opportunities for developing an online, publicly available automated tool for extremism data collection and detection. The survey results in conceptualization of architecture for construction of multi-ideology extremism text dataset with robust data validation techniques for multiclass classification of extremism text.