Online Library

Extremism has been a problematic term to define and suggests different and opposing meanings. This study explores how Twitter users conceptualize extremism in Arabic and express their opinions/arguments to construct the term. A corpus of tweets was collected from Twitter API using the word ‘تطرف أو متطرف’ in Arabic for extremist/extremism. A topic modeling algorithm was then applied to the dataset to uncover latent associated concepts underlying extremism, followed by a critical discourse analysis using Van Dijk’s Sociocognitive approach. The discursive and linguistic strategies used by Twitter users to support and justify their views of extremism were examined. The findings demonstrate an ideological influence that controls the concept of extremism, keeping it open to manipulation to serve shared interests and goals. Arab users of Twitter use extremism to promote their groups’ positive schema against others-negative schema.

The public outcry attending President Trump’s attempted travel ban from seven radical Muslim states, designed to prevent foreign terrorists from entering the country, has diverted attention from the longstanding danger of homegrown jihadists. As early as 2007, the New York Police Department (NYPD) released a 92-page report documenting the extent of al-Qaeda-linked homegrown jihad in Europe and the United States.[1] The Obama administration, however, went out of its way to ignore, deny, and whitewash any homegrown terror that smacked of Islamist violence. But a decade later, al-Qaeda has been all but eclipsed by the Islamic State (ISIS), which has skillfully used social media to become the foremost purveyor of jihadist indoctrination in the West, creating a “virtual caliphate,” extremely dangerous and easily accessible to vulnerable men and women from a variety of backgrounds in a manner al-Qaeda was never able to achieve. Even were all territory now under ISIS control to be retaken, this virtual caliphate could continue to pose a major threat.

Book review: This edited volume offers a collection of essays on the timely topic of online violent extremism, including: terrorists’ wide ranging use of online platforms, the appeal of online platforms, internet use in terrorism trials, online extremist narrative and counter-narrative studies, the need for and the challenges associated with online violent extremism research particularly from an audience perspective.

Scholars have long seen radicalisation as a predominantly group based phenomenon, occurring largely through ‘real world,’ in person interaction. By contrast, the internet is seen as playing only a limited ‘facilitating’ role in radicalising people to violence. However, a series of attacks by far right extremists over the past decade has demonstrated that this perspective is less accurate than it once was. Almost none of these terrorists were members of extremist groups and had only engaged with other extremists on the internet. In this article, we examine the relative importance of face-to-face group interaction and physically isolated internet-based radicalization in driving individuals towards extremist violence. We do so through a detailed case study of Action Zealandia, New Zealand’s leading ideological white nationalist group. The study is based on eighteen months of infiltration of the group by one of the authors from 2019 to 2021. When interacting online, members often adopt highly extremist personas, in some cases threatening mass violence. By contrast, face to face interaction and group membership pushed the group away from extremist violence. This was due to several factors: police pressure and a lack of opportunity for the movement to grow, and the often uninspiring nature of offline interaction.

The advent of the Internet inadvertently augmented the functioning and success of violent extremist organizations. Terrorist organizations like the Islamic State in Iraq and Syria (ISIS) use the Internet to project their message to a global audience. The majority of research and practice on web‐based terrorist propaganda uses human coders to classify content, raising serious concerns such as burnout, mental stress, and reliability of the coded data. More recently, technology platforms and researchers have started to examine the online content using automated classification procedures. However, there are questions about the robustness of automated procedures, given insufficient research comparing and contextualizing the difference between human and machine coding. This article compares output of three text analytics packages with that of human coders on a sample of one hundred nonindexed web pages associated with ISIS. We find that prevalent topics (e.g., holy war) are accurately detected by the three packages whereas nuanced concepts (Lone Wolf attacks) are generally missed. Our findings suggest that naïve approaches of standard applications do not approximate human understanding, and therefore consumption, of radicalizing content. Before radicalizing content can be automatically detected, we need a closer approximation to human understanding.

This research examines the relationship between race and exposure to online hate material. The utilization of websites, weblogs, newsgroups, online games, radio broadcasts, online newsletters and a myriad of other online platforms has proliferated race-based hate groups in the US (Shafer 2002). According to the Southern Poverty Law Center (SPLC), the number of hate groups has been on the rise since the 1990s and continues to gain momentum with the advent of social media (Potok 2017). Exposure to separatist ideologies has propelled these radical rightwing groups into the mainstream by way of social media platforms, as they are “the most active producers of online hate material” (Costello, Hawdon, Ratliff, and Grantham 2016: pg. 313). That dissemination of radical rightwing ideologies, such as white supremacy, racial purity, and racial solidarity, exists is not enough in understanding what individuals are exposed to race-based hate ideologies in online platforms. Exposure is the key to understanding the growth of these race-based hate groups and ways of countering the efforts to disseminate radical rightwing ideologies due to its relationship to hate group emergence and persistence. More so, understanding how these groups target individuals and recruit through social networking sites can provide insight into exposure. Exposure to hate material aids groups in recruiting new members and victimizing potential targets. In the same manner, exposure to hate material is victimization of those who are exposed. In a sample collected by Costello et al. (2016a), of those exposed to hate material online nearly half centered on race. Thus, it is tantamount that research is conducted examining the role that race plays in determining who is exposed to hate material online, and how individuals react to hate material based on race. This dissertation will examine the importance of exposure to hate. Specifically, this dissertation will analyze survey data gathered from the Online Extremism Survey using logistic regression analysis and linear regression to understand exposure to hate material online and routine activity theory.