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Lone-wolf terrorism is a threat to the security of modern society, as was tragically shown in Norway on July 22, 2011, when Anders Behring Breivik carried out two terrorist attacks that resulted in a total of 77 deaths. Since lone wolves are acting on their own, information about them cannot be collected using traditional police methods such as infiltration or wiretapping. One way to attempt to discover them before it is too late is to search for various ‘‘weak signals’’ on the Internet, such as digital traces left in extremist web forums. With the right tools and techniques, such traces can be collected and analyzed. In this work, we focus on tools and techniques that can be used to detect weak signals in the form of linguistic markers for potential lone wolf terrorism.

Since the inception of the World Wide Web, security agencies, researchers, and analysts have focused much of their attention on the sentiment found on hate-inspired web-forums. Here, one of their goals has been to detect and measure users’ affects that are expressed in the forums as well as identify how users’ affects change over time. Manual inspection has been one way to do this; however, as the number of discussion posts and sub-forums increase, there has been a growing need for an automated system that can assist humans in their analysis. The aim of this paper, then, is to detect and measure a number of affects expressed in written text on Stormfront.org, the most visited hate forum on the Web. To do this, we used a machine learning approach where we trained a model to recognize affects on three sub-forums: Ideology and Philosophy, For Stormfront Ladies Only, and Stormfront Ireland. The training data consisted of manual annotated data and the affects we focused on were racism, aggression, and worries. Results indicate that even though measuring affects is a subjective process, machine learning is a promising way forward to analyze and measure the presence of different affects on hate forums.

This report presents research carried out within the project (Ku2016/01373/D – Uppdrag till Totalförsvarets forskningsinstitut (FOI) att göra kartläggningar och analyser av våldsbejakande extremistisk propaganda) that has been assigned to the Swedish defence research agency by the Swedish Government. The project will continue until March 2019. The purpose of this report is to highlight various aspects of IS’s digital propaganda; How it is spread, what its content is, how the content varies over time, and how images are used in the propaganda. We have also studied the role of women and children in IS-propaganda, and how a youth culture called Jihadi cool has evolved with its own kind of clothing, music and cultural expressions. IS’s propaganda contains a variety of religious concepts and references, which IS sometimes use in a non-traditional way. Therefore, a chapter in this report describes how Islamic politico-religious language can be used to convey different kinds of messages

The purpose of this report is to raise awareness of how the so-called Islamic State (IS) uses digital propaganda to reach followers in the West. Although IS has suffered significant territorial losses, the digital battle is far from over. The big social media platforms are now effective in removing propaganda, but despite this, IS is always finding new ways of producing and spreading its messages. IS ability to adapt to an ever-changing digital landscape is likely to keep the organization alive by enabling communication between supporters worldwide. This report provides an introduction to IS propaganda from different perspectives: the specific language used by IS, the brand IS, the magazines produced by IS and women’s role in IS.

This paper discusses the use of computer support to analyze IS propaganda. We focus on thematic analysis of the textual content of various types of online propaganda, and investigate two different ways to identify themes in the data. One approach is theory-driven and builds on previous analysis of IS propaganda, and the other approach is data-driven and automatically identifies prevalent themes in the data. We exemplify the various approaches using different types of IS propaganda and we also provide examples of how the computer-assisted analysis can be used to analyze variation in the data over time. Both approaches indicate an increase in more violent themes in more recent IS propaganda.