Loud, Proud, and Extreme: Understanding the Group Processes of Far-Right Groups

A special issue of Social Sciences (ISSN 2076-0760).

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 5021

Special Issue Editors


E-Mail Website
Guest Editor
Sociology Department, Louisiana State University, Baton Rouge, LA 70803, USA
Interests: the socio-spatial dynamics of gang behavior; effective strategies aimed at reducing neighborhood violence and discouraging gang activity
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Criminology & Criminal Justice, UNC Charlotte, Charlotte, NC 28223, USA
Interests: street gangs; white power youth; crime; violence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

Perceived threats remain a principal driver sustaining far-right groups. Dynamics internal to far-right groups and external dynamics between far-right groups and/or rival groups (e.g., Antifa) remains understudied. Much of the conventional literature on the far-right remains focused on either the individual (micro-level) or on ideology (macro-level), ignoring the social processes of the group (meso-level). Additionally, the ubiquity of digital technology, particularly social media platforms, has greatly influenced how far-right groups operate today. Online violence, whether threats, taunts, or posting of violent acts that took place, is much less understood. This knowledge gap includes the link between the online activities of far-right groups and how it can manifest into real-world action. This issue will examine the diverse nature of far-right groups, which include anti-government “militias” (e.g., Oath Keepers), accelerationists (e.g., Boogaloo Bois), neo-Nazi terror groups (e.g., Atomwaffen Division, The Base), alt-right gangs (e.g., Proud Boys), and sovereign citizen collectives, with the goal of better understanding the growing complexities of far-right to better inform public policy solutions. Given the dynamic nature of far-right groups today, this Special Issue encourages empirical research (quantitative, qualitative, or mixed-methods) with multi-/interdisciplinary perspectives from around the world that highlights cutting-edge approaches to examining far-right groups. Papers may be theoretical and/or empirical in nature. All submissions will be considered; however, primary consideration will be given to manuscripts that:

  • Investigate the relationship between online activity and real-world violence;
  • Examine the efficacy of deradicalization/desistance programs;
  • Engage in cross comparisons of far-right groups by either location or type;
  • Take innovative approaches to advancing our understanding of far-right violence.

Dr. Matthew Valasik
Dr. Shannon E. Reid
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a double-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Social Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • far-right
  • white power
  • right-wing extremism
  • white supremacy
  • violence
  • nativism
  • nationalism
  • intervention
  • public policy

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

19 pages, 2374 KiB  
Article
Method for Detecting Far-Right Extremist Communities on Social Media
by Anna Karpova, Aleksei Savelev, Alexander Vilnin and Sergey Kuznetsov
Soc. Sci. 2022, 11(5), 200; https://doi.org/10.3390/socsci11050200 - 02 May 2022
Cited by 4 | Viewed by 4366
Abstract
Far-right extremist communities actively promote their ideological preferences on social media. This provides researchers with opportunities to study these communities online. However, to explore these opportunities one requires a way to identify the far-right extremists’ communities in an automated way. Having analyzed the [...] Read more.
Far-right extremist communities actively promote their ideological preferences on social media. This provides researchers with opportunities to study these communities online. However, to explore these opportunities one requires a way to identify the far-right extremists’ communities in an automated way. Having analyzed the subject area of far-right extremist communities, we identified three groups of factors that influence the effectiveness of the research work. These are a group of theoretical, methodological, and instrumental factors. We developed and implemented a unique algorithm of calendar-correlation analysis (CCA) to search for specific online communities. We based CCA on a hybrid calendar correlation approach identifying potential far-right communities by characteristic changes in group activity around key dates of events that are historically crucial to those communities. The developed software module includes several functions designed to automatically search, process, and analyze social media data. In the current paper we present a process diagram showing CCA’s mechanism of operation and its relationship to elements of automated search software. Furthermore, we outline the limiting factors of the developed algorithm. The algorithm was tested on data from the Russian social network VKontakte. Two experimental data sets were formed: 259 far-right communities and the 49 most popular (not far-right) communities. In both cases, we calculated the type II error for two mutually exclusive hypotheses—far-right affiliation and no affiliation. Accordingly, for the first sample, β = 0.81. For the second sample, β = 0.02. The presented CCA algorithm was more effective at identifying far-right communities belonging to the alt-right and Nazi ideologies compared to the neo-pagan or manosphere communities. We expect that the CCA algorithm can be effectively used to identify other movements within far-right extremist communities when an appropriate foundation of expert knowledge is provided to the algorithm. Full article
Show Figures

Figure 1

Back to TopTop