Journal Article |
Conversations with other (alt-right) women: How do alt-right female influencers narrate a far-right identity?
View Abstract
In the process of shifting far-right ideas from the fringes to the centre of the political spectrum, the alt-right has infiltrated online spaces to mainstream extremist ideas. As part of this process, female alt-right influencers have emerged within various popular social media platforms and fringe outlets, seeking to build credibility for the movement with new audiences. Contrary to previous assumptions about women as harmless adherents of far-right ideology, alt-right women are emerging as “organic intellectuals”, influential in the formation of everyday beliefs and principles in congruence with the tenets of far-right ideology. Their narratives strategically weave far-right ideological discourses, such as the imminent crisis of white identity, with topical matters on lifestyle and well-being. This article examines the rhetoric of online influencers as they shape an ideological space which is contributing to the normalization or mainstreaming of far-right ideas. In doing so, it addresses two questions: How do alt-right female influencers narrate a far-right identity? How do they mainstream white supremacist ideas online? Drawing on new empirical material from a series of far-right podcasts, this article demonstrates that alt-right women strategically construct a “liberated” female identity rooted in femininity, traditionalism and gender complementarity, and problematize feminism and women’s emancipation as constitutive of the crisis facing the white race. It further identifies the presence of an elaborate cultural narrative around white victimhood which alt-right influencers use to mainstream their ideology. To counter the perpetuation of far-right ideas in society, women’s participation in shaping far-right ideology should not remain unaddressed. This article sheds some light on how a small but highly visible group of influencers are actively working to promote a dangerous far-right ideology.
|
2022 |
Maria-Elena, K., Yannick, V.L. and Vanessa, N. |
View
Publisher
|
Journal Article |
Far-Right ‘Reactions’: a comparison of Australian and Canadian far-right extremist groups on Facebook
View Abstract
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.
|
2022 |
Hutchinson, J. and Droogan, J. |
View
Publisher
|
Chapter |
Birds of a Feather: A Comparative Analysis of White Supremacist and Violent Male Supremacist Discourses
View Abstract
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.
|
2022 |
Pruden, M.L., Lokmanoglu, A.D., Peterscheck, A. and Veilleux-Lepage, Y. |
View
Publisher
|
Journal Article |
A semi-supervised algorithm for detecting extremism propaganda diffusion on social media
View Abstract
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.
|
2022 |
Francisco, M., Benítez-Castro, M.Á., Hidalgo-Tenorio, E. and Castro, J.L. |
View
Publisher
|
Journal Article |
From image to function Automated analysis of online jihadi videos
View Abstract
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.
|
2022 |
García-Marín, J. and Luengo, Ó.G. |
View
Publisher
|
MA Thesis |
From Traits to Threats – Identification of Personality Traits for Individuals at Risk of Radicalisation on Social Media
View Abstract
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.
|
2022 |
Underhaug, L.M. |
View
Publisher
|