Online Library

This article’s objective is to categorize potential jihadists in Europe and provide an overview of contemporary methods for their recruitment. Taking into account the ubiquity of the Internet and social networking platforms, and the fact that younger generations are spending an ever increasing amount of time using contemporary communication technology, this article focuses on those recruitment methods that make use of social networking platforms and mobile applications for the spread of extremist propaganda, as well as for communication with potential adherents. An analysis of the age structure of individuals involved in the planning and carrying out of terrorist acts in Europe from November of 2015 to September of 2017 supports a hypothesis that contemporary recruitment methods are especially effective in targeting a younger demographic. In addition, this article negates the importance of traditional physical exposure to radical behavior, which serves to explain the increasing number of terrorist attacks in Europe conducted by radicalized citizens of European countries.

Online discussion forums provide open workspace allowing users to share information, exchange ideas, address problems, and form groups. These forums feature multimodal posts and analyzing them requires a framework that can integrate heterogeneous information extracted from the posts, i.e. text, visual content and the information about user interactions with the online platform and each other. In this paper, we develop a generic framework that can be trained to identify communication behavior and patterns in relation to an entity of interest, be it user, image or text in internet forums. As the case study we use the analysis of violent online political extremism content, which has been a major challenge for domain experts. We demonstrate the generalizability and flexibility of our framework in predicting relational information between multimodal entities by conducting extensive experimentation around four practical use cases.

Incels are a sub-culture community of men who forge a sense of identity around their perceived inability to form sexual or romantic relationships. In recent years, there has been a small, but growing, number of violent attacks that have been attributed to individuals who identify as incels. The purpose of this study was to use a large sample of incels from the UK and US to establish (a) their demographic make-up; (b) the consistency of their attitudes and beliefs; (c) their adherence to a common world view, (d) how they network with other incels; (e) whether there are cross-cultural differences between incels from the UK and US in the above; and finally, whether there is a predictive relationship between incel mental health, networking and ideology and the extent of their harmful attitudes and beliefs.

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.

Exposure to hate material is related to a host of negative outcomes. Young people might be especially vulnerable to the deleterious effects of such exposure. With that in mind, this article examines factors associated with the frequency that youth and young adults, ages 15 to 24, see material online that expresses negative views toward a social group. We use an online survey of individuals recruited from a demographically balanced sample of Americans for this project. Our analysis controls for variables that approximate online routines, social, political, and economic grievances, and sociodemographic traits. Findings show that spending more time online, using particular social media sites, interacting with close friends online, and espousing political views online all correlate with increased exposure to online hate. Harboring political grievances is likewise associated with seeing hate material online frequently. Finally, Whites are more likely than other race/ethnic groups to be exposed to online hate frequently.

It is now widely understood that extremists use the Internet in attempts to accomplish many of their objectives. In this chapter we present a web-crawler called the Terrorism and Extremism Network Extractor (TENE), designed to gather information about extremist activities on the Internet. In particular, this chapter will focus on how TENE may help differentiate terrorist websites from anti-terrorist websites by analyzing the context around the use of predetermined keywords found within the text of the webpage. We illustrate our strategy through a content analysis of four types of web-sites. One is a popular white supremacist website, another is a jihadist website, the third one is a terrorism-related news website, and the last one is an official counterterrorist website. To explore differences between these websites, the presence of, and context around 33 keywords was examined on both websites. It was found that certain words appear more often on one type of website than the other, and this may potentially serve as a good method for differentiating between terrorist websites and ones that simply refer to terrorist activities. For example, words such as “terrorist,” “security,” “mission,” “intelligence,” and “report,” all appeared with much greater frequency on the counterterrorist website than the white supremacist or the jihadist websites. In addition, the white supremacist and the jihadist websites used words such as “destroy,” “kill,” and “attack” in a specific context: not to describe their activities or their members, but to portray themselves as victims. The future developments of TENE are discussed.