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Given the proliferation of violent extremist content online in recent years, developing effective counter-narratives – messages that offer a positive alternative to extremist propaganda, or deconstruct or delegitimise extremist narratives and challenge extremist ideologies – is an increasingly necessary alternative to online censorship. This Handbook, funded by Public Safety Canada through the Kanishka Project, was created by the Institute for Strategic Dialogue (ISD) to help anyone looking to proactively respond to extremist propaganda with counter-narrative campaigns, and is intended as a beginner’s guide for those with little or no previous experience of counter-narrative campaigning. It takes readers through the main stages of creating, launching and evaluating an effective c ounter-narrative c ampaign. I t c an a lso b e u sed a longside I SD’s freely available online Counter-narrative Toolkit, which can be found at www.counternarratives.org. Our advice is based on ISD’s experiences in creating, running and evaluating in-house campaigns such as Extreme Dialogue, and collaborating with independent content-creators, from civil society and NGO campaigners to young activists, to amplify their counter-narrative messages through training, networking and campaign support. This Handbook therefore focuses on civil-society, youth or NGO-led online counternarrative campaigns.

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.

With the rise of the Islamic State (IS), a great deal of attention has recently been drawn to two issues that have come to be seen as intricately linked: the role of women within pro-jihadist networks (Lahoud 2014; Hoyle, Bradford and Frenett 2015; Saltman and Smith 2015) and the use of social media as an indoctrination and recruitment tool by terrorist organizations (Weimann 2008; Bloom 2013; Klausen 2015). The present working paper is an attempt at improving our understanding of women’s participation in online jihadist networks through examination of the nature and scope of their activities within pro-IS and pro-AQ Twitter networks. To explore that participation, we look at women’s activities online through two different methodological lenses: a. by looking at gender in relation to female tweeting patterns; and b. by examining the content of women’s posts within and across two distinct pro-jihadist networks, one associated with IS, and the other comprised of women from various al-Qaeda (AQ)-affiliated groups.

The terms Deep Web, Deep Net, Invisible Web, or Dark Web refer to the content on the World Wide Web that is not indexed by standard search engines. The deepest layers of the Deep Web, a segment known as the Dark Web, contain content that has been intentionally concealed including illegal and anti-social information. The conventional Surface Web was discovered to be too risky for anonymity-seeking terrorists: they could be monitored, traced, and found. In contrast, on the Dark Web, decentralized and anonymous networks aid in evading arrest and the closure of these terrorist platforms. This paper reports some of the recent trends in terrorist use of the Dark Web for communication, fundraising, storing information and online material.

This paper presents the first criminological analysis of an online social reaction to a crime event of national significance, in particular the detection and propagation of cyberhate on social media following a terrorist attack. We take the Woolwich, London terrorist attack in 2013 as our event of interest and draw on Cohen’s process of warning, impact, inventory and reaction to delineate a sequence of incidents that come to constitute a series of deviant responses following the attack. This paper adds to contemporary debates in criminology and the study of hate crime in three ways: (1) it provides the first analysis of the escalation, duration, diffusion and de-escalation of cyberhate in social media following a terrorist event; (2) it applies Cohen’s work on action, reaction and amplification and the role of the traditional media to the online context and (3) it introduces and provides a case study in ‘computational criminology’.

Hateful and antagonistic content published and propagated via the World Wide Web has the potential to cause harm and suffering on an individual basis, and lead to social tension and disorder beyond cyber space. Despite new legislation aimed at prosecuting those who misuse new forms of communication to post threatening, harassing, or grossly offensive language – or cyber hate – and the fact large social media companies have committed to protecting their users from harm, it goes largely unpunished due to difficulties in policing online public spaces. To support the automatic detection of cyber hate online, specifically on Twitter, we build multiple individual models to classify cyber hate for a range of protected characteristics including race, disability and sexual orientation. We use text parsing to extract typed dependencies, which represent syntactic and grammatical relationships between words, and are shown to capture ‘othering’ language – consistently improving machine classification for different types of cyber hate beyond the use of a Bag of Words and known hateful terms. Furthermore, we build a data-driven blended model of cyber hate to improve classification where more than one protected characteristic may be attacked (e.g. race and sexual orientation), contributing to the nascent study of intersectionality in hate crime.