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This article reflects on two core issues of human subjects’ research ethics and how they play out for online extremism and terrorism researchers. Medical research ethics, on which social science research ethics are based, centers the protection of research subjects, but what of the protection of researchers? Greater attention to researcher safety, including online security and privacy and mental and emotional wellbeing, is called for herein. Researching hostile or dangerous communities does not, on the other hand, exempt us from our responsibilities to protect our research subjects, which is generally ensured via informed consent. This is complicated in data-intensive research settings, especially with the former type of communities, however. Also grappled with in this article therefore are the pros and cons of waived consent and deception and the allied issue of prevention of harm to subjects in online extremism and terrorism research. The best path forward it is argued—besides talking through the diversity of ethical issues arising in online extremism and terrorism research and committing our thinking and decision-making around them to paper to a much greater extent than we have done to-date—may be development of ethics guidelines tailored to our sub-field.

VOX-Pol’s new report presents findings from the REASSURE (Researcher, Security, Safety, and Resilience) project’s in-depth interviews with 39 online extremism and terrorism researchers. Based at universities, research institutes, and think tanks in Europe and North America, the interviewees studied mainly, albeit not exclusively, far-right and violent jihadist online activity. The report catalogues for the first time the range of harms they have experienced, the lack of formalised systems of care or training, and their reliance therefore on informal support networks to mitigate those harms.

The Islamic State of Iraq and ash-Sham (ISIS) continues to use social media as an essential element of its campaign to motivate support. On Twitter, ISIS’ unique ability to leverage unaffiliated sympathizers that simply retweet propaganda has been identified as a primary mechanism in their success in motivating both recruitment and “lone wolf” attacks. The present work explores a large community of Twitter users whose activity supports ISIS propaganda diffusion in varying degrees. Within this ISIS supporting community, we observe a diverse range of actor types, including fighters, propagandists, recruiters, religious scholars, and unaffiliated sympathizers. The interaction between these users offers unique insight into the people and narratives critical to ISIS’ sustainment. In their entirety, we refer to this diverse set of users as an online extremist community or OEC. We present Iterative Vertex Clustering and Classification (IVCC), a scalable analytic approach for OEC detection in annotated heterogeneous networks, and provide an illustrative case study of an online community of over 22,000 Twitter users whose online behavior directly advocates support for ISIS or contibutes to the group’s propaganda dissemination through retweets.

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.

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.