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Extreme, anti-establishment actors are being characterized increasingly as ‘dangerous individuals’ by the social media platforms that once aided in making them into ‘Internet celebrities’. These individuals (and sometimes groups) are being ‘deplatformed’ by the leading social media companies such as Facebook, Instagram, Twitter and YouTube for such offences as ‘organised hate’. Deplatforming has prompted debate about ‘liberal big tech’ silencing free speech and taking on the role of editors, but also about the questions of whether it is effective and for whom. The research reported here follows certain of these Internet celebrities to Telegram as well as to a larger alternative social media ecology. It enquires empirically into some of the arguments made concerning whether deplatforming ‘works’ and how the deplatformed use Telegram. It discusses the effects of deplatforming for extreme Internet celebrities, alternative and mainstream social media platforms and the Internet at large. It also touches upon how social media companies’ deplatforming is affecting critical social media research, both into the substance of extreme speech as well as its audiences on mainstream as well as alternative platforms.

This paper reports on a study that is part of a project which aims to develop a multimodal analytical approach for big data analytics, initially in the context of violent extremism. The findings reported here tested the application of natural language processing models to the text of a sample of articles from the online magazines Dabiq and Rumiyah, produced by the Islamic extremist organisation ISIS. For comparison, text of articles found by reverse image search software which re-used the lead images from the original articles in text which either reported on or opposed extremist activities was also analysed. The aim was to explore what insights the natural language processing models could provide to distinguish between texts produced as propaganda to incite violent extremism and texts which either reported on or opposed violent extremism. The results showed that some valuable insights can be gained from such an approach and that these results could be improved through integrating automated analyses with a theoretical approach with analysed language and images in their immediate and social contexts. Such an approach will inform the interpretation of results and will be used in training software so that stronger results can be achieved in the future.

This article begins by outlining four post-WWII strategies of right-wing revolutionary resistance: vanguardism; the cell system; leaderless resistance; and metapolitics. Next, the article argues that metapolitics became a preferred strategy for many right-wing revolutionaries during the 2000s and early 2010s, and proposes three conditions that may help explain this metapolitical turn: limited opportunities for armed resistance; a subcultural style shift; and new opportunities for promoting alternative worldviews online. Finally, the article theorizes about the types of threats that may emerge in the wake of this metapolitical turn, and speculates about the likelihood of a new and more violent turn in the near future.

This report aims to contribute to our understanding of online hate in the context of the requirements of the revised Audiovisual Media Services Directive (AVMSD) for Video Sharing Platforms (VSPs) to protect the general public from incitement to hatred or violence. However, online hate is complex and it can only be fully understood by considering issues beyond the very specific focus of these regulations. Hence, we draw on recent social and computational research to consider a range of points outside VSP regulations, such as the impact, nature and dynamics of online hate. For similar reasons,
we have considered expressions of hate across a range of online spaces, including VSPs as well as other online platforms. In particular, we have closely examined how online hate is currently addressed by industry, identifying key and emerging issues in content moderation practices. Our analyses will be relevant to a range of experts and stakeholders working to address online hate, including researchers, platforms, regulators and civil society organisations.

Digital platforms enabled the disturbing rise of domestic extremism, culminating with the January 6 attack on the U.S. Capitol. Militia groups use social media networks to plan operations, recruit new members, and spread anti-democracy propaganda, a new Digital Citizens Alliance (Digital Citizens) and Coalition for a Safer Web (CSW) investigation has found.

Purpose – This chapter demonstrates the power that Google, Apple, Facebook, Amazon and Microsoft (or the “GAFAM”) exercise over platforms within society, highlights the alt-right’s use of GAFAM sites and services as a platform for hate, and examines GAFAM’s establishment and use of hate content moderation apparatuses to de-platform alt-right users and delete hate content. Approach – Drawing upon a political economy of communications approach, this chapter demonstrates GAFAM’s power in society. It also undertakes a reading of GAFAM “terms of service agreements” and “community guidelines” documents to identify GAFAM hate content moderation apparatuses. Findings – GAFAM are among the most powerful platforms in the world, and their content moderation apparatuses are empowered by the US government’s cyber-libertarian approach to Internet law and regulation. GAFAM are defining hate speech, deciding what’s to be done about it, and censoring it. Value – This chapter probes GAFAM’s hate content moderation apparatuses for Internet platforms, and shows how GAFAM enable and constrain the alt-right’s hate speech on their platforms. It also reflexively assesses the politics of empowering GAFAM to de-platform the alt-right.