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Employing social network analysis, this article investigates  the  transnational communication network and discourse of political-Salafists on social media. It examines whether political-Salafists across the MENA region have a common sociopolitical and geopolitical agenda, and whether – given the recent shift of some political-Salafists towards violence – their discourse and communication network can still be distinguished from that of the jihadists. The analysis finds that political-Salafists do not share a common agenda but that their discourse and communication network display three transnational gravity centres: a revisionist, a status quo-oriented and an ostracized pro-Sisi gravity centre. Only the revisionist gravity centre advocates violence. Its discourse, however, remains clearly set apart from that of the jihadists.

There is a tendency in scholarly and strategic-policy fields to see the propaganda produced by groups like Islamic State and Al-Qaeda as historically unheralded. As evidence, slickly produced communiques and a penchant for using social media are typically highlighted. This narrow perspective, in placing the current phenomenon into an historical and thematic vacuum, infers that history has little to offer contemporary efforts to understand and confront extremist propaganda. This research paper explores the history of propaganda during conflict and draws out key lessons for improving counterterrorism strategic communications. Overall, history suggests that a strategic communications campaign during conflict is more likely to succeed if it based on a multifaceted approach characterised by the deployment of a diversity of messages that leverage a variety of target audience motivations, uses all pertinent means of communication (not just the latest), and synchronises this messaging with strategicpolicy/politico-military actions.

The Redirect Method​ uses Adwords targeting tools and curated YouTube videos uploaded by people all around the world to confront online radicalization. It focuses on the slice of ISIS’ audience that is most susceptible to its messaging, and redirects them towards curated YouTube videos debunking ISIS recruiting themes. This open methodology was developed from interviews with ISIS defectors, respects users’ privacy and can be deployed to tackle other types of violent recruiting discourses online.

Though aiming to remedy genuine harms, government regulation of our online lives also raises legitimate concerns over privacy and freedom of expression. We must address online harms whilst ensuring harms are not also inflicted through unfairly infringing on people’s freedoms. HOPE not hate recognises the importance of this balancing act, and encourages a form of regulation of platforms that places democratic rights front-and-centre. In a world increasingly infused with the web, the significance of this legislation cannot be overstated and it is undoubtedly the case that getting it right will take rigorous reflection. To that end, we encourage debate of the recommendations proposed in this report.

Countering online hate speech is a critical yet challenging task, but one which can be aided by the use of Natural Language Processing (NLP) techniques. Previous research has primarily focused on the development of NLP methods to automatically and effectively detect online hate speech while disregarding further action needed to calm and discourage individuals from using hate speech in the future. In addition, most existing hate speech datasets treat each post as an isolated instance, ignoring the conversational context. In this paper, we propose a novel task of generative hate speech intervention, where the goal is to automatically generate responses to intervene during online conversations that contain hate speech. As a part of this work, we introduce two fully-labeled large-scale hate speech intervention datasets collected from Gab and Reddit. These datasets provide conversation segments, hate speech labels, as well as intervention responses written by Mechanical Turk Workers. In this paper, we also analyze the datasets to understand the common intervention strategies and explore the performance of common automatic response generation methods on these new datasets to provide a benchmark for future research.