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Fear is one of the negative outcomes of terrorist attacks. Currently, there is a need to understand how societal fear and fear of terrorism might be shaped and induced by social-media discussions. This study analyzed how exposure to cyberhate was associated with perceived societal fear after the November 2015 Paris terrorist attacks. Demographically balanced data sets were collected from France, Spain, Finland, Norway, and the United States four weeks after the attacks. Cyberhate exposure was associated with higher perceived societal fear in all countries studied even when adjusting for confounding factors. This was particularly evident in the case of cyberhate related to terrorism. Hateful online communication after disruptive events may contribute to a social climate of fear and escalate societal uncertainty. There are, however, indications that social trust may bolster against perceived societal fear, hence enhancing resilience.

Far right populist politicians and movements have secured high levels of visibility thanks to often compliant media outlets and unregulated digital platforms. The pursuit of media coverage and the communication of rage are no longer incidental but essential to the growth of reactionary populisms. Yet, while prominent liberal media outlets are ‘aghast’ at events such as the election of Donald Trump and the Brexit vote, little attention has been paid to the structural conditions and policy frameworks that have facilitated the circulation of clickbait and misinformation, together with the incessant coverage of their leaders, that have been exploited by far right movements. This article identifies four areas of ‘media policy failure’ that have nurtured highly skewed media environments and concludes by calling for a new policy paradigm based around redistribution that aims to reconstruct media systems in order to undermine the appeal of populist forces on the far right.

Researchers have previously explored how right-wing extremists build a collective identity online by targeting their perceived “threat,” but little is known about how this “us” versus “them” dynamic evolves over time. This study uses a sentiment analysis-based algorithm that adapts criminal career measures, as well as semi-parametric group-based modeling, to evaluate how users’ anti-Semitic, anti-Black, and anti-LGBTQ posting behaviors develop on a sub-forum of the most conspicuous white supremacy forum. The results highlight the extent to which authors target their key adversaries over time, as well as the applicability of a criminal career approach in measuring radical posting trajectories online.

Violent extremist activity on social media in the Philippines is a relatively new phenomenon in the complex conflict environment that exists in the southern part of the country. The rise of online violent extremism emerged despite the Philippines’ significant strides in the Mindanao peace process. The 2014 Comprehensive Agreement on the Bangsamoro between the government and the MILF was a landmark achievement. Yet a number of armed groups rejected the deal, many of which are now engaged in online extremism. The apparent affiliation of these groups with issues beyond Mindanao and the Philippine state signaled a potential new era of conflict in the country. With these concerns as a backdrop, The Asia Foundation and Rappler worked together to explore how young Filipinos interact with social media networks, and look into the prevalence and characteristics of violent extremist messaging and recruitment in the Philippines in 2018.

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.