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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.

With the spread of social networks and their unfortunate use for hate speech, automatic detection of the latter has become a pressing problem. In this paper, we reproduce seven state-of-the-art hate speech detection models from prior work, and show that they perform well only when tested on the same type of data they were trained on. Based on these results, we argue that for successful hate speech detection, model architecture is less important than the type of data and labeling criteria. We further show that all proposed detection techniques are brittle against adversaries who can (automatically) insert typos, change word boundaries or add innocuous words to the original hate speech. A combination of these methods is also effective against Google Perspective – a cutting edge solution from industry. Our experiments demonstrate that adversarial training does not completely mitigate the attacks, and using character-level features makes the models systematically more attack-resistant than using word-level features.

As with so many technologies, the Internet’s racism was programmed right in—and it’s quickly fueled the spread of White supremacist, xenophobic rhetoric throughout the western world.

A new wave of growing antisemitism, driven by fringe Web communities, is an increasingly worrying presence in the socio-political realm. The ubiquitous and global nature of the Web has provided tools used by these groups to spread their ideology to the rest of the Internet. Although the study of antisemitism and hate is not new, the scale and rate of change of online data has impacted the efficacy of traditional approaches to measure and understand this worrying trend. In this paper, we present a large-scale, quantitative study of online antisemitism. We collect hundreds of million comments and images from alt-right Web communities like 4chan’s Politically Incorrect board (/pol/) and the Twitter clone, Gab. Using scientifically grounded methods, we quantify the escalation and spread of antisemitic memes and rhetoric across the Web. We find the frequency of antisemitic content greatly increases (in some cases more than doubling) after major political events such as the 2016 US Presidential Election and the “Unite the Right” rally in Charlottesville. Furthermore, this antisemitism appears in tandem with sharp increases in white ethnic nationalist content on the same communities. We extract semantic embeddings from our corpus of posts and demonstrate how automated techniques can discover and categorize the use of antisemitic terminology. We additionally examine the prevalence and spread of the antisemitic “Happy Merchant” meme, and in particular how these fringe communities influence its propagation to more mainstream services like Twitter and Reddit. Taken together, our results provide a data-driven, quantitative framework for understanding online antisemitism. Our open and scientifically grounded methods serve as a framework to augment current qualitative efforts by anti-hate groups, providing new insights into the growth and spread of antisemitism online.

The perceived threat of extremist online propaganda has generated a need for countermeasures applicable to large audiences. The dissemination of videos designed to counter violent extremism (CVE videos) is widely discussed. These videos are often described as “counter-narratives,” implying that narrativity is a crucial factor for their effectiveness. Experimental research testing this assumption is rare and direct comparisons of narrativity effects between propaganda and CVE videos are lacking. To fill this gap, we conducted two experiments (one in a laboratory and one online) in which we confronted German participants with different religious affiliations and from various cultural backgrounds (NStudy 1 = 338 and NStudy 2 = 155) with Islamist extremist or right-wing extremist propaganda videos and with corresponding CVE videos. The results confirmed that narrativity (a) increases persuasive processing of propaganda and CVE videos, (b) fosters amplification intentions regarding these videos, and (c) increases attraction to extremists versus counter-activists. Thus, our studies highlight the crucial role of narrativity in both extremist propaganda and video-based CVE approaches.

A new revolution has begun in counterterrorism—the Artificial Intelligence (AI) revolution. The AI revolution has had a significant impact on many areas of security and intelligence. The use of AI and big data in general, and in the field of intelligence and counterterrorism in particular, has led to intense debates between supporters of the continuation and expansion of the use of this technology and those who oppose it. The traditional delicate balance between effectiveness in the fight against terrorism and the liberal democratic values of society becomes even more crucial when counterterrorism engages in AI and big data technology