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Little is known about which features of Facebook’s interface appeal to users of far-right extremist groups, how such features may influence a user’s interpretation of far-right extremist themes and narratives, and how this is being experienced across various nations. This paper looks at why certain ‘Reactions’ appealed to users in Australian and Canadian far-right groups on Facebook, and how these ‘Reactions’ may have characterized user decisions during their interaction with far-right extremist themes and narratives. A mixed methods approach has been used to conduct a cross-national comparative analysis of three years of ‘Reaction’ use across 59 Australian and Canadian far-right extremist groups on Facebook (2016–2019). The level of user engagement with administrator posts was assessed using ‘Reactions’ and identified themes and narratives that generated the most user engagement specific to six ‘Reactions’ ( ‘Love’, ‘Haha’, ‘Wow’, ‘Sad’, ‘Angry’ and ‘Thankful’). This was paired with an in-depth qualitative analysis of the themes and narratives that attracted the most user engagement specific to two popular ‘Reactions’ used over time ( ‘Angry’ and ‘Love’). Results highlight ‘Angry’ and ‘Love’ as the two most popular ‘Reactions’ assigned to in-group-out-group themes and narratives, with ‘ algorithms having propelled their partnership in these groups.

This chapter explores the intersection of white and male supremacy, both of which misrepresent women as genetically and intellectually inferior and reduce them to reproductive and/or sexual functions. The white power movement historically has been characterized by sexism and misogyny, as evidenced by the movement’s attempts to retain European heritage and maintain whiteness by policing the behavior and controlling the bodies of white women. However, the influence of white supremacist discourses on physically violent manifestations of the male supremacist movement remains largely understudied. Using supervised machine learning, we compare a corpus of violent male supremacist manifestos and other multimodal content with highly influential white nationalist texts and the manifestos of violent white supremacists to identify the shared beliefs, tropes and justifications for violence deployed within.

Extremist online networks reportedly tend to use Twitter and other Social Networking Sites (SNS) in order to issue propaganda and recruitment statements. Traditional machine learning models may encounter problems when used in such a context, due to the peculiarities of microblogging sites and the manner in which these networks interact (both between themselves and with other networks). Moreover, state-of-the-art approaches have focused on non-transparent techniques that cannot be audited; so, despite the fact that they are top performing techniques, it is impossible to check if the models are actually fair. In this paper, we present a semi-supervised methodology that uses our Discriminatory Expressions algorithm for feature selection to detect expressions that are biased towards extremist content (Francisco and Castro 2020). With the help of human experts, the relevant expressions are filtered and used to retrieve further extremist content in order to iteratively provide a set of relevant and accurate expressions. These discriminatory expressions have been proved to produce less complex models that are easier to comprehend, and thus improve model transparency. In the following, we present close to 70 expressions that were discovered by using this method alongside the validation test of the algorithm in several different contexts.

The strategy of jihadist groups is based on objectives that are sometimes global. Specifically, many of these groups argue that Muslims, wherever they live, should fight for the establishment of an Islamic state or, at least, for such a state to be possible elsewhere. Therefore, taking advantage of the emergence of the Internet, they initiated an equally universal narrative strategy, with the production of a great deal of content, especially audiovisual texts. The effects of this material are known and, unfortunately, may be behind the terrorist actions of various individuals in many countries. Hence the concern of academics lies with their analyses and with the development of methodologies that can successfully deal with large amounts of multimodal information. The present research, therefore, aims to apply a quantitative procedure to the analysis of jihadist propaganda. Specifically, the authors have analysed 2,211 videos belonging to different terrorist groups, by applying an image classification algorithm. The results show that this type of approach has realistic possibilities of providing relevant information about this corpus – when realized, they may help to create automated analytical tools capable of dealing with the enormous amount of information that can be disseminated on-line.

This Thesis contributes by proposing a method for identifying users believed to be at risk of radicalisation on social media, by utilising the social media networks of already radicalised individuals and a set of indicators derived from related work on radicalisation. In addition, this Thesis provides a new to the field, in-depth analysis of the personality traits of Twitter users at risk of radicalisation and how they may differ from ordinary users. The results show that the proposed data collection and annotation scheme is able to successfully identify individuals at risk of radicalisation, yielding an inter-annotator agreement, measured by Cohen’s Kappa, of 0.83. The analysis of the predicted personality traits shows that users at risk of radicalisation have common profiles for agreeableness and conscientiousness. When comparing the predicted traits to that of ordinary, non-radical Twitter users, the predictions show a marginal difference in distribution for agreeableness, openness, and conscientiousness, indicating a certain difference in personality between the two domains.

This study examined the literature on online terrorism studies between 2001 and 2022 and compared themes across journal segments and funded research. The results showed that there is a relationship between funding and the number of publications in recent years. While themes in the core terrorism journals more likely to follow the traditional terrorism studies, funded research (e.g. Computer Science Journals) focus more on social media, far-right groups and hate speech utilizing new research tools such as machine learning and text mining techniques. These new tools enable researchers to study different dimensions of the phenomenon, but these works are not published in terrorism journals. The results also point to a gap between core terrorism journals and Computer Science journals, which heralds at least two potential developments in the future: i) emergence of specialized journals focusing on terrorism and concepts like social media, populism, hate speech, text mining, machine learning and so on; and ii) more collaborative outputs between terrorism researchers and computer scientists.