Online Library

Objectives
Despite popular notions of “filter bubbles” and “echo chambers” contributing to radicalization, little evidence exists to support these hypotheses. However, social structure social learning theory would suggest a hereto untested interaction effect.
Methodology
An RCT of new Twitter users in which participants were randomly assigned to a treatment of “filter bubble” (personalization algorithm) suppression. Ego-centric network and survey data were combined to test the effects on justification for suicide bombings.
Findings
Statistically significant interaction effects were found for two proxies of the echo chamber, the E-I index and modularity. For the treatment group, higher scores on both factors decreased the likelihood for radicalization, with opposing trends in the control group.
Conclusions
The echo chamber effect may be dependent on the filter bubble. More research is needed on online network structures in radicalization. While personalization algorithms can potentially be harmful, they may also be leveraged to facilitate interventions.

Faced with YouTube’s continuous refusal to engage, Mozilla built a browser extension, RegretsReporter, that allows people to donate data about the YouTube videos they regret watching on the platform. Through one of the biggest crowdsourced investigations into YouTube, we’ve uncovered new information that warrants urgent attention and action from lawmakers and the public. We now know that YouTube is recommending videos that violate their very own content policies and harm people around the world — and that needs to stop.

This book constitutes a journey into the obscure field of sectarian-guided discourses of radical Islamist groups. It provides new insights into the ideological mechanisms utilized by such organizations to incite sectarian conflicts and recruit local and foreign guardians to their alleged cause. This book examines diverse aspects and dimensions of the discourses of Sunni-based ISIS and Shia-based al-Hashd al-Shaabi and explores manipulative and ideological discursive strategies utilized by media outlets associated with these groups. It delves into linguistic and contextual activities, implicit and explicit messages within the discourses of various media outlets operating in the heart of the Middle East. It also scrutinizes and explains aspects of politicization, religionization and sectarianization within the media discourse of terrorist groups in the digital era.

This article presents an original analysis of portrayals of women in Bosnia and Herzegovina (BiH) on websites with radical and extremist content. We analyze and discuss key online narratives about women and their societal roles from 100 sampled online articles published on ten websites that primarily propagate right-wing, nationalist and Salafist thinking. The article shows that while Salafist websites focus on didactic material about how women are expected to act (selectively using the Quran and hadiths), radical ethno-nationalist and right-wing articles directly accuse liberal-thinking women and feminists of immorality, perversity, and shameful behavior. While studied articles on Islamic websites confine women’s roles to family caretakers and to wives, selected ethno-nationalist articles present women in relation to their national belonging and war victimization. We conclude that the studied websites essentialize women’s societal functions, predominantly placing their roles and identities in the private sphere while linking womanhood to collective national and/or religious identity. Although the selected platforms represent different manifestations of extremisms, they share many similarities.

I am interested in contributing further knowledge regarding the alt-right, white supremacy, and the Internet by exploring the sinister conducting of Zoom-bombing anti-racist events. Here, I will investigate how white supremacy through the Internet can lead to violence, abuse, and fear that “transcends the virtual world to damage real, live humans beings” via Zoom-bombing, an act that is situated in a larger co-option of the Internet by the alt-right and white supremacists, but has been under theorised as a hate crime.

Policymakers have recently expressed concerns over the role of recommendation algorithms and their role in forming “filter bubbles”. This is a particularly prescient concern in the context of extremist content online; these algorithms may promote extremist content at the expense of more moderate voices. In this article, we make two contributions to this debate. Firstly, we provide a novel empirical analysis of three platforms’ recommendation systems when interacting with far-right content. We find that one platform—YouTube—does amplify extreme and fringe content, while two—Reddit and Gab—do not. Secondly, we contextualise these findings into the regulatory debate. There are currently few policy instruments for dealing with algorithmic amplification, and those that do exist largely focus on transparency. We argue that policymakers have yet to fully understand the problems inherent in “de-amplifying” legal, borderline content and argue that a co-regulatory approach may offer a route towards tackling many of these challenges.