Online Library

This paper reviews online gender-based violence (OGBV) as existing within a continuum of (on- and offline) violence, emphasising the connections with different extremist ideologies, including the dissemination of terrorist and violent extremist content (TVEC). It aims to prioritise a gender perspective in responding to TVEC so that social media platforms can better intervene in and mitigate misogynistic pathways to radicalisation that can begin (or be reinforced) online. The discussion recognises that the mitigation of OGBV and online pathways to radicalisation requires a whole-of-society and whole-of-government approach. Whilst there are steps that governments and civil society can and should take, such as overseeing and enforcing emerging regulatory frameworks and voluntary commitments, this paper and its recommendations emphasise the role and actions of platforms.

This Strategic Studies Institute book provides a comprehensive research guide to radical Islamist English-language online magazines published between April-May 2007 and November 2016, as well as Islamic State eBooks published between November 2012 and November 2015, and a number of assorted radical Islamist news magazines, reports, and pocketbooks. A comparative analysis of Inspire and Dabiq magazine issues are provided along with strategic insights related to al-Qaeda and Islamic State online magazine clusters and their differing strategic approaches as articulated in these magazines. Finally, policy response options, utilizing a targeting schema leveraging the five stages of the magazine life-cycle process: environmental motivators, production, end product, distribution, and outcomes, are discussed.

VOX-Pol Blog Post.

Anno Bunnik explores the ramifications of Big Data for countering terrorism, extremism, and radicalisation. With the recent rise of jihadists and other extremists groups in the Middle East and Europe, how do state agencies respond through the use of Big Data? This chapter critically engages with questions such as the extent to which Big Data can inform us about and help prevent radicalisation. It deals with the role of non-governmental organisations and the private sector in this regard. Bunnik makes the argument that whilst it is inevitable that Big Data will take centre stage in counter-terrorism, its potential should also be utilised to better understand radicalisation and extremism.

Anno Bunnik explores the ramifications of Big Data for countering terrorism, extremism, and radicalisation. With the recent rise of jihadists and other extremists groups in the Middle East and Europe, how do state agencies respond through the use of Big Data? This chapter critically engages with questions such as the extent to which Big Data can inform us about and help prevent radicalisation. It deals with the role of non-governmental organisations and the private sector in this regard. Bunnik makes the argument that whilst it is inevitable that Big Data will take centre stage in counter-terrorism, its potential should also be utilised to better understand radicalisation and extremism.

The use of “Big Data” in policy and decision making is a current topic of debate. The 2013 murder of Drummer Lee Rigby in Woolwich, London, UK led to an extensive public reaction on social media, providing the opportunity to study the spread of online hate speech (cyber hate) on Twitter. Human annotated Twitter data was collected in the immediate aftermath of Rigby’s murder to train and test a supervised machine learning text classifier that distinguishes between hateful and/or antagonistic responses with a focus on race, ethnicity, or religion; and more general responses. Classification features were derived from the content of each tweet, including grammatical dependencies between words to recognize “othering” phrases, incitement to respond with antagonistic action, and claims of well-founded or justified discrimination against social groups. The results of the classifier were optimal using a combination of probabilistic, rule-based, and spatial-based classifiers with a voted ensemble meta-classifier. We demonstrate how the results of the classifier can be robustly utilized in a statistical model used to forecast the likely spread of cyber hate in a sample of Twitter data. The applications to policy and decision making are discussed.