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The anti-terror public media campaigns started in Iraq around 2004 and was called ‘Terror has no Religion’ in order to combat the threats of sectarianism and Al-Qaeda. After the withdrawal of the US forces from the country in late 2010, the campaign stopped, but a new and similar one emerged that is called ‘Say no to Terror’ whose advertisements mostly targeted the Saudi public. Several Pan-Arab regional channels like Al-Arabiya and Middle East Broadcasting Center (MBC) were part of airing its advertisements. This study focuses on the ‘Terror has no Religion’ and ‘Say no to Terror’ campaigns by critically examining their websites and videos to understand the nature of messages sent to the public. Further, the study examines the effectiveness of the two campaigns with special emphasis on ‘Say no to Terror’ by analyzing comments posted on YouTube and discussing the counter campaign. Over 350 videos were found containing counter arguments to ‘Say no to Terror’ campaign, and about 60% of YouTube commentators viewed the campaign negatively, expressing suspicion about its real intentions. The paper concludes that the success of such public service advertisements is doubtful due to the format of the message as well as cultural and political reasons that are linked to the region.

VOX-Pol Blog post.

The presence of hate speech in the commentary field of online newspapers is a pressing challenge for free speech policy. We have conducted interviews with 15 people whose comments were censored for posting comments of a racist, discriminatory or hateful nature. What characterizes their self-understanding and enemy images? We found that central to their motivation for writing such comments was an understanding of themselves as particularly knowledgeable people. They see themselves as people who fight for the revelation of the truth, in contrast to the lies spread by politicians and the media. Furthermore, they regard politicians and the media as corrupt elites that are leading our society into destruction by their naïve support of liberal migration policies. By linking up to alternative news media, these individuals support various forms of racialized conspiracy theories, but also a form of radical right-wing populism in their concern that politics should be acted out by people themselves. As such, our study adds to the literature on conspiracy theories in general and racialized conspiracy theories in particular, but also to the literature on online far-right activists. Our contribution lies both in the newness of focusing on the self-perceptions, but also in opening up for a modification of existing literature on the far right.

Until his death in a U.S. drone strike in August 2015, Junaid Hussain was the Islamic State’s most prolific Englishlanguage social media propagandist, working to incite and guide sympathizers in the United Kingdom, United States, and beyond to launch terrorist attacks. Before joining the
jihad in Syria, Hussain was part of a hacking collective in the United Kingdom, focusing much of his attention on perceived injustices against Muslims. In many respects, he was well integrated into British society with his family home in a leafy suburb of Birmingham. A spell in prison contributed to his radicalization and his decision to move to Syria, where he married fellow extremist Sally Jones.

We present a machine learning framework that leverages a mixture of metadata, network, and temporal features to detect extremist users, and predict content adopters and interaction reciprocity in social media. We exploit a unique dataset containing millions of tweets generated by more than 25 thousand users who have been manually identified, reported, and suspended by Twitter due to their involvement with extremist campaigns. We also leverage millions of tweets generated by a random sample of 25 thousand regular users who were exposed to, or consumed, extremist content. We carry out three forecasting tasks, (i) to detect extremist users, (ii) to estimate whether regular users will adopt extremist content, and finally (iii) to predict whether users will reciprocate contacts initiated by extremists. All forecasting tasks are set up in two scenarios: a post hoc (time independent) prediction task on aggregated data, and a simulated real-time prediction task. The performance of our framework is extremely promising, yielding in the different forecasting scenarios up to 93% AUC for extremist user detection, up to 80% AUC for content adoption prediction, and finally up to 72% AUC for interaction reciprocity forecasting. We conclude by providing a thorough feature analysis that helps determine which are the emerging signals that provide predictive power in different scenarios.

On 14 May 2022, an attack was carried out in Buffalo, New York, which resulted in the death of ten individuals and the wounding of three others. The attack was livestreamed online and versions of the footage were disseminated on multiple online services, potentially exposing UK users to content related to terrorism.

As the regulator of UK-established video-sharing platforms (VSPs) and the prospective UK online safety regulator, we sought to learn from the tragic event by reviewing industry responses to the livestreamed attack and cross-industry collaboration to prevent dissemination of associated content.