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Radicalisation can take place offline as well as online. To what extent the internet plays a role remains contested. Doubtless, there are radical and extreme communities online. This report looked into the ability of artificial intelligence (AI) applications to contribute to countering radicalisation. Mapping the possibilities and limitations of this technology in its various forms, the report aims to support decision‑makers and experts navigate the noise, leading to informed decisions unswayed by the current hype.

Internet Communication Technologies (ICTs) have become increasingly popular for social movements of all kinds. Social movement organizations (SMOs) use them to interact and mobilize also across borders. Previous research has highlighted the opportunities for transnational networking and the resulting reduction in the importance of local dependency for SMOs. One movement that combines local direct action with the goal of an international network of activists is Antifa. Single case studies have highlighted that local demonstrations and networks are still vital for antifascist groups. However, studies that examine the influence of spatial dynamics on building an international (digital) network beyond focusing on a single country are still lacking. This study, therefore, examines the impact of proximity on forming digital connections among 355 unique European antifascist groups. Using Exponential Random Graph Modeling (ERGM), we examine the hyperlink network across Europe via the groups’ online presence. The findings reveal that the languages of the groups and the geographical distance between them are significant predictors for the probability of displayed hyperlink connections. These results demonstrate the relevance of proximity theories in the field of digital activism. The study contributes to a deeper understanding of spatial dynamics in shaping online networks, reinforcing the importance of space in contemporary network analysis in social movements studies.

In Part I, this Note will look at the European Union’s current Internet Forum and their proposed Terrorist Content Regulation. The proposed regulation focuses on countering terrorist content on social media by placing much of the burden on the social media companies themselves. Part II will focus on the Material Support of Foreign Terrorist Groups Statue: 18U.S.C.§ 2339(b).

This paper presents a survey on hate speech detection. Given the steadily growing body of social media content, the amount of online hate speech is also increasing. Due to the massive scale of the web, methods that automatically detect hate speech are required. Our survey describes key areas that have been explored to automatically recognize these types of utterances using natural language processing. We also discuss limits of those approaches.

The far right is increasingly relying on visual and less extreme online communication, for instance by using memes, to strategically mainstream their ideology. The use of humor in particular renders their communication more relatable to a mainstream audience. However, little is known about the actual impacts of the different content characteristics they employ to become more appealing, in particular on less moderated platforms that function as safe online spaces for extremist ideology and contents. To fill this gap, we conducted a manual quantitative content analysis of 1,200 memes distributed within German-language far-right Telegram channels in 2020 and 2021, concentrating on humor and several content-related factors to analyze their impact on meme reach. The results demonstrate that memes with extreme far-right narratives and memes with humor received fewer views than others, but that memes with both far-right narratives and humor had a significantly increased reach. The findings highlight the mainstreaming potential of humor, particularly when used to mask extreme content that would otherwise be less appealing.

Although many social media users have reported encountering hate speech, differences in the perception between different users remain unclear. Using a qualitative multi-method approach, we investigated how personal characteristics, the presentation form, and content-related characteristics influence social media users’ perceptions of hate speech, which we differentiated as first-level (i.e. recognizing hate speech) and second-level perceptions (i.e. attitude toward it). To that end, we first observed 23 German-speaking social media users as they scrolled through a fictitious social media feed featuring hate speech. Next, we conducted remote self-confrontation interviews to discuss the content and semi-structured interviews involving interactive tasks. Although it became apparent that perceptions are highly individual, some overarching tendencies emerged. The results suggest that the perception of and indignation toward hate speech decreases as social media use increases. Moreover, direct and prosecutable hate speech is perceived as being particularly negative, especially in visual presentation form.