Online Library

Noon Briefing by Stephanie Dujarric, Spokesman for the Secretary General

The Strong Cities Network (SCN) conducted an online mapping exercise to investigate the main extremist narratives deployed in North Macedonia. Using a mixed method of automated collection and expert manual qualitative online research, SCN identified the main narratives across political, ethnic and religiously inspired extremist and hate groups. On the basis of these findings, SCN provided a comprehensive set of recommendations for national government, local authorities, civil society and the private tech sector to help inform a more comprehensive response to these online harms. The findings from this study will also inform multi-agency work the network is carrying out with the community.

In this chapter, we explore the communication modes and tactics used by extremists across the ideological spectrum to spread their narratives on social media. We argue that a growing body of evidence suggests that extremist movements across the political spectrum make use of similar communication modes and tactics on social media to reach their objectives. Drawing on specific case studies and examples from extreme-right, conspiracy myth and Islamist extremist campaigns, this chapter highlights how groups and networks adapt to changing circumstances and external pressure by making different strategic choices about whether to use overt or covert, centralized or crowdsourced, influencer-based or network-based and aggressive or optics-focused communication modes. On a tactical level, our case studies provide insights into approaches chosen by extremist movements to radicalize sympathizers, influence mainstream audiences and intimidate political opponents. Throughout these case studies, it becomes clear that extremists are engaged in collective learning in relation to their communication modes and the use of new technologies, with approaches and instructional materials being shared and inspiring extremists across borders.

This study compares the narratives and language of QAnon groups in the encrypted messaging apps Telegram and Discord to those observed in the manifestos of terrorists. Drawing on our systematic linguistic analysis of fifteen terrorist manifestos that were published in the past decade, we developed a coding scheme which traces the narratives and linguistic markers that occur in the written communication of perpetrators of political violence. In this pilot study we apply our new coding scheme to QAnon content to assess the scale and nature of violence-associated narratives within the movement. Based on 200,000 messages that we collected from the online QAnon group “Great Awakening Community” on the gaming chat application Discord, we quantitatively examine to what degree they carry the trademarks of violent terrorist manifestos that are not found in non-violent texts. We then compared the results for the Great Awakening Community to content from both a non-violent and a violent-terrorist control group. To complement our computational assessment of QAnon narrative and linguistic patterns we share ethnographic observations from ten QAnon Telegram and Discord groups with English, German, and French speaking audiences. Past research has found that identity fusion in combination with a range of mediating and moderating variables is a strong predictor of violence in groups, and this is further supported by our terrorist manifesto analysis. Our study of QAnon messages found a high prevalence of linguistic identity fusion indicators along with external threat narratives, violence-condoning group norms as well as demonizing, dehumanizing, and derogatory vocabulary applied to the out-group, especially when compared to the non-violent control group. The aim of this piece of research is twofold: (i.) It seeks to evaluate the national security threat posed by the QAnon movement, and (ii.) it aims to provide a test of a novel linguistic toolkit aimed at helping to assess the risk of violence in online communication channels.

Germany’s Network Enforcement Act, or NetzDG law represents a key test for combatting hate speech on the internet. Under the law, which came into effect on January 1, 2018, online platforms face fines of up to €50 million for systemic failure to delete illegal content. Supporters see the legislation as a necessary and efficient response to the threat of online hatred and extremism. Critics view it as an attempt to privatise a new ‘draconian’ censorship regime, forcing social media platforms to respond to this new painful liability with unnecessary takedowns. This study shows that the reality is in between these extremes. NetzDG has not provoked mass requests for takedowns. Nor has it forced internet platforms to adopt a ‘take down, ask later’ approach. At the same time, it remains uncertain whether NetzDG has achieved significant results in reaching its stated goal of preventing hate speech. This paper begins by explaining the background that led to the development and passage of NetzDG. It examines the reaction to the law by civil society, platforms and the government. It concludes with suggestions, for platforms, civil society and the authorities, on ways to improve the law to be effective in the fight against online hate while keeping the internet open and free.

In the context of the 2016 U.S. Presidential Election, President Donald Trump’s use of Twitter to connect with followers and supporters created unprecedented access to Trump’s online political campaign. In using the campaign slogan, “Make America Great Again” (or its acronym “MAGA”), Trump communicatively organized and controlled media systems by offering his followers an opportunity to connect with his campaign through the discursive hashtag. In effect, the strategic use of these networks over time communicatively constituted an effective and winning political organization; however, Trump’s political organization was not without connections to far-right and hate groups that coalesced in and around the hashtag. Semantic network analyses uncovered how the textual nature of #MAGA organized connections between hashtags, and, in doing so, exposed connections to overtly White supremacist groups within the United States and the United Kingdom throughout late November 2016. Cluster analyses further uncovered semantic connections to White supremacist and White nationalist groups throughout the hashtag networks connected to the central slogan of Trump’s presidential campaign. Theoretically, these findings contribute to the ways in which hashtag networks show how Trump’s support developed and united around particular organizing processes and White nationalist language, and provide insights into how these networks discursively create and connect White supremacists’ organizations to Trump’s campaign.