There and Back Again: How White Nationalist Ephemera Travels Between Online and Offline Spaces

Berger, Jonathan; Aryaeinejad, Kateira; Looney, Seán

doi:10.1080/03071847.2020.1734322

Cited by 12 publications

(7 citation statements)

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“…This finding aligns with previous work which found that nonviolent RWEs were more prolific posters than violent RWEs (see Scrivens, Wojciechowski, et al, 2021). Empirical research on the online behaviors of RWEs similarly found those who committed right-wing violence offline are usually concerned that law enforcement officials and anti-racist groups are monitoring their online activities and may (1) modify or limit their posting behaviors (e.g., Gaudette et al, 2020) and/or (2) use encrypted platforms to avoid detection (e.g., Berger et al, 2020; Guhl & Davey, 2020). Research also suggests that violent members of RWE movements are largely clandestine, often paranoid, and seek to avoid drawing attention to themselves (e.g., Blee & Creasap, 2010; Gruenewald et al, 2009; Hamm, 1993; Perry & Scrivens, 2019; Simi & Futrell, 2015).…”

Section: Discussionmentioning

confidence: 99%

Differentiating Online Posting Behaviors of Violent and Nonviolent Right-Wing Extremists

Scrivens

Wojciechowski

Freilich

et al. 2022

Criminal Justice Policy Review

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There is an ongoing need for researchers, practitioners, and policymakers to detect and assess online posting behaviors of violent extremists prior to their engagement in violence offline, but little is empirically known about their online behaviors generally or the differences in their behaviors compared with nonviolent extremists who share similar ideological beliefs particularly. In this study, we drew from a unique sample of violent and nonviolent right-wing extremists to compare their posting behaviors in the largest White supremacy web-forum. We used logistic regression and sensitivity analysis to explore how users’ time of entry into the lifespan of an extremist sub-forum and their cumulative posting activity predicted their violence status. We found a number of significant differences in the posting behaviors of violent and nonviolent extremists which may inform future risk factor frameworks used by law enforcement and intelligence agencies to identify credible threats online.

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Section: Discussionmentioning

confidence: 99%

Differentiating Online Posting Behaviors of Violent and Nonviolent Right-Wing Extremists

Scrivens

Wojciechowski

Freilich

et al. 2022

Criminal Justice Policy Review

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“…In other words, we lack an understanding of how online extremism changed compared with traditional extremism. This issue exceeds the prevalent calls for investigating the relationship between on-and offline extremism (Berger et al, 2020;Bliuc et al, 2019;Herath & Whittaker, 2021) or whether the internet actually had an exacerbating impact on extremism and terrorism (for an overview of the discussion, see Conway, 2017;Khalil, 2021). Instead, we propose exploring online extremism and how digital technologies complement or change strategies, structures and methods of value creation (Vial, 2019), what types of digitally enabled organizations emerge (Hanelt et al, 2021), and how movements differ regarding their degrees of digitalization (Strohmeier, 2020).…”

Section: Directing Online Extremism Research With the Sociotechnical ...mentioning

confidence: 94%

The digital augmentation of extremism: Reviewing and guiding online extremism research from a sociotechnical perspective

Risius

Blasiak

Wibisono

et al. 2023

Information Systems Journal

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Online extremism remains a persistent problem despite the best efforts of governments, tech companies and civil society. Digital technologies can induce group polarization to promote extremism and cause substantial changes to extremism (e.g., create new forms of extremism, types of threats or radicalization approaches). Current methods to counter extremism induce undesirable side‐effects (e.g., ostracize minorities, inadvertently promote extremism) or do not leverage the full potential of digital technologies. Extremism experts recognize the need for researchers from other disciplines, like information systems, to contribute their technical expertise for understanding and countering online extremism. This article aims to introduce the field of information systems to the issue of online extremism. Information systems scholars address technology‐related societal issues from a sociotechnical perspective. The sociotechnical perspective describes systems through a series of interactions between social (structure, people) and technical components (physical system, task). We apply the sociotechnical perspective to (1) summarize the current state‐of‐the‐art knowledge of 222 articles in a systematic multi‐disciplinary literature review and (2) propose specific research questions that address two questions (How do digital technologies augment extremism? How can we successfully counter online extremism?).

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“…In addition, the keywords such as "white genocide," "white lives matter," "it's okay to be white," and "anti-white" were used to collect White supremacist-related tweets. Tese White supremacist supporting keywords were obtained from [74][75][76].…”

Section: Data Extraction From Twittermentioning

confidence: 99%

Multi‐Ideology, Multiclass Online Extremism Dataset, and Its Evaluation Using Machine Learning

Gaikwad

Ahirrao

Phansalkar

et al. 2023

Computational Intelligence and Neuroscience

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Social media platforms play a key role in fostering the outreach of extremism by influencing the views, opinions, and perceptions of people. These platforms are increasingly exploited by extremist elements for spreading propaganda, radicalizing, and recruiting youth. Hence, research on extremism detection on social media platforms is essential to curb its influence and ill effects. A study of existing literature on extremism detection reveals that it is restricted to a specific ideology, binary classification with limited insights on extremism text, and manual data validation methods to check data quality. In existing research studies, researchers have used datasets limited to a single ideology. As a result, they face serious issues such as class imbalance, limited insights with class labels, and a lack of automated data validation methods. A major contribution of this work is a balanced extremism text dataset, versatile with multiple ideologies verified by robust data validation methods for classifying extremism text into popular extremism types such as propaganda, radicalization, and recruitment. The presented extremism text dataset is a generalization of multiple ideologies such as the standard ISIS dataset, GAB White Supremacist dataset, and recent Twitter tweets on ISIS and white supremacist ideology. The dataset is analyzed to extract features for the three focused classes in extremism with TF-IDF unigram, bigrams, and trigrams features. Additionally, pretrained word2vec features are used for semantic analysis. The extracted features in the proposed dataset are evaluated using machine learning classification algorithms such as multinomial Naïve Bayes, support vector machine, random forest, and XGBoost algorithms. The best results were achieved by support vector machine using the TF-IDF unigram model confirming 0.67 F1 score. The proposed multi-ideology and multiclass dataset shows comparable performance to the existing datasets limited to single ideology and binary labels.

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There and Back Again: How White Nationalist Ephemera Travels Between Online and Offline Spaces

Cited by 12 publications

References 0 publications

Differentiating Online Posting Behaviors of Violent and Nonviolent Right-Wing Extremists

Differentiating Online Posting Behaviors of Violent and Nonviolent Right-Wing Extremists

The digital augmentation of extremism: Reviewing and guiding online extremism research from a sociotechnical perspective

Multi‐Ideology, Multiclass Online Extremism Dataset, and Its Evaluation Using Machine Learning

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