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This project by GNET looks at the user journeys of individuals who enter and participate in the online spaces of extremist communities. A user journey here refers to the distinct path a user may follow to reach their goals when accessing and using an online space belonging to extremist communities.

User journeys are particularly important in offering insights into the rationale and motivations of users on the one hand, and to the inner workings of extremist online communities on the other. This is vital for understanding their goals and objectives.

In selecting the ideologies for this project, we drew upon extremist communities – rather than extremist and terrorist organisations or groups – including those actors that participate in the extremist milieu and share ideas but do not necessarily operate in concert. These ideologies include those of formal and well‐defined extremist organisations of White supremacist and anti‐government extremist groups in the United States, supporter networks of Islamic State (IS), and looser communities of extremist actors, including accelerationists, incels and chan site members who operate on shared platforms, congregating around common beliefs without the connection of formal membership.

This project is a response to the growing interest in understanding how individuals enter and participate in online spaces of extremist communities. A core goal of the project was to understand the role of algorithms in leading users to extremist communities, including the changes in algorithmic recommendations that lead users to more extreme content online. However, examining these changes proved impossible due to the precautions taken by the expert contributors to the project, such as the use of separate technology and VPNs throughout their research.

The project also highlights the distinct posting behaviour and operational security protocols of different groups, usually along ideological lines.

The potential of terrorists and other enemies to use the Internet and/or other digital information technologies as transport vehicles of weapons of mass destruction (WMD) is becoming more evident. This threat may involve cyber-assisted biochemical transactions, which can be performed through olfactory information exchanges, to attack civilian and military targets anytime. In response, this research is the product of a four-year investigation wherein the researcher went through a rich archive of declassified U.S. military reports (covering biochemical warfare and techno-terrorism)and a selective list of patent applications revealing several concepts related to users’ adoption of emergent cyber-assisted olfaction and aroma-embedded information systems. And after a rigorous analysis of the findings, the researcher hypothesized that careless adoption of unsafe technologies could accelerate a cyber-WMD-nexus.

Thus, the study conducted in support of this research sought to examine and explain the adoption of emergent cyber-assisted olfactory information technologies. Cyber-assisted olfaction is an emergent technology that involves the replication of the human sense of smell by computer systems. It can be manifested in the stimulation of human olfactory receptors by computer-emitted biochemical or aromatic particles. Certain olfaction-based biosensors, known as electronic noses, mimic the sense of smell to detect chemical plumes (aroma or warfare agents) in a variety of settings. Notably, the literature revealed an innovative dimension of multimodal, affective human-computer interactions involving scented emails, aroma therapy, and olfaction-based marketing. Unfortunately, the lack of security and safety persists at a potentially catastrophic cost, which is at the root of the researcher’s stated research problem below.

Largely supported by Everett Rogers’ innovation diffusion theory (IDT) and Fred Davis’ technology acceptation model (TAM), the researcher went beyond the “smell-o-vision” aphorism to offer a fertile ground for a new, comprehensive, and flexible framework to understand various aspects of cyber-assisted olfactory information systems, which may be usefully applied to a wide range of auxiliary information systems (i.e., standard, micro, and nano devices). In relation to national security and defense, the proposed Model for Safer Cyber-Assisted Olfaction (MSCAO) can be applied in subway systems, aircrafts, cruise ships, and various mixing/distribution systems (i.e., water systems and chemical plants) to automatically detect and neutralize a biological or chemical threat. Many military applications are also anticipated for emerging urban warfare and emergency rescue operations using devices such as miniature robots and nano air vehicles (NAVs).

Consistent with the aim of this investigation, the researcher used a mixedmethods approach that combined (1) the content analysis of a series of declassified military reports on airborne warfare agents with that of (2) a patent search on aroma embedded information systems to examine, with the help of a carefully designed survey instrument, the perceptions of potential adopters of the proposed MSCAO. Further, in order to collect data and obtain statistical evidence linked to the selected variables (perceived credibility, ease-of-use, and usefulness), the researcher surveyed a diverse group of students from a major Engineering School in southeastern United States, which offers degree programs in Mechanical, Electrical, Chemical and Biomedical Engineering. Based on the analysis performed, credibility, ease-of-use, and usefulness were found to be significant enough to be considered as positively influencing the acceptability of the MSCAO. In essence, the respondents expressed a significantly high level of need for harm-reduction and cyber security measures against potential threats and considered the MSCAO as meeting their need.

Perceived usefulness (67%) was identified as the principal determinant of the respondents’ intention to adopt the proposed model. Though less substantial than perceived usefulness, perceived ease-of-use (56%) appeared to have a greater influence on the MSCAO than perceived credibility (54%). In sum, extrapolating from the findings reported herein, the researcher concluded that potential users are more likely to accept an emergent technology that is useful, easy-to-use, and that offers credible harm-reduction measures.

VOX-Pol Blog post.

Recent terrorist attacks by first- and second-generation immigrants in the United States and Europe indicate that radicalization may result from the failure of ethnic integration—or the rise of intergroup prejudice in communities where “home-grown” extremists are raised. Yet, these community-level drivers are notoriously difficult to study because public opinion surveys provide biased measures of both prejudice and radicalization. We examine the relationship between anti-Muslim and pro-ISIS (Islamic State of Iraq and Syria) Internet searches in 3099 U.S. counties between 2014 and 2016 using instrumental variable models that control for various community-level factors associated with radicalization. We find that anti-Muslim searches are strongly associated with pro-ISIS searches—particularly in communities with high levels of poverty and ethnic homogeneity. Although more research is needed to verify the causal nature of this relationship, this finding suggests that minority groups may be more susceptible to radicalization if they experience discrimination in settings where they are isolated and therefore highly visible—or in communities where they compete with majority groups for limited financial resources. We evaluate the validity of our findings using several other data sources and discuss the implications of our findings for the study of terrorism and intergroup relations, as well as immigration and counterterrorism policies.

Twitter is the largest and most popular micro-blogging website on Internet. Due to low publication barrier, anonymity and wide penetration, Twitter has become an easy target or platform for extremists to disseminate their ideologies and opinions by posting hate and extremism promoting tweets. Millions of tweets are posted on Twitter everyday and it is practically impossible for Twitter moderators or an intelligence and security analyst to manually identify such tweets, users and communities. However, automatic classification of tweets into pre-defined categories is a non-trivial problem problem due to short text of the tweet (the maximum length of a tweet can be 140 characters) and noisy content (incorrect grammar, spelling mistakes, presence of standard and non-standard abbreviations and slang). We frame the problem of hate and extremism promoting tweet detection as a one-class or unary-class categorization problem by learning a statistical model from a training set containing only the objects of one class . We propose several linguistic features such as presence of war, religious, negative emotions and offensive terms to discriminate hate and extremism promoting tweets from other tweets. We employ a single-class SVM and KNN algorithm for one-class classification task. We conduct a case-study on Jihad, perform a characterization study of the tweets and measure the precision and recall of the machine-learning based classifier. Experimental results on large and real-world dataset demonstrate that the proposed approach is effective with F-score of 0.60 and 0.83 for the KNN and SVM classifier respectively.

VOX-Pol Blog Post.