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The purpose of this paper is to explore the use of community question answering sites (CQAs) on the topic of terrorism. Three research questions are investigated: what are the dominant themes reflected in terrorism-related questions? How do answer characteristics vary with question themes? How does users’ anonymity relate to question themes and answer characteristics?

Data include 300 questions that attracted 2,194 answers on the community question answering Yahoo! Answers. Content analysis was employed.

The questions reflected the community’s information needs ranging from the life of extremists to counter-terrorism policies. Answers were laden with negative emotions reflecting hate speech and Islamophobia, making claims that were rarely verifiable. Users who posted sensitive content generally remained anonymous.

This paper raises awareness of how CQAs are used to exchange information about sensitive topics such as terrorism. It calls for governments and law enforcement agencies to collaborate with major social media companies to develop a process for cross-platform blacklisting of users and content, as well as identifying those who are vulnerable.

Theoretically, it contributes to the academic discourse on terrorism in CQAs by exploring the type of questions asked, and the sort of answers they attract. Methodologically, the paper serves to enrich the literature around terrorism and social media that has hitherto mostly drawn data from Facebook and Twitter.

Islamophobic hate speech on social media is a growing concern in contemporary Western politics and society. It can inflict considerable harm on any victims who are targeted, create a sense of fear and exclusion amongst their communities, toxify public discourse and motivate other forms of extremist and hateful behavior. Accordingly, there is a pressing need for automated tools to detect and classify Islamophobic hate speech robustly and at scale, thereby enabling quantitative analyses of large textual datasets, such as those collected from social media. Previous research has mostly approached the automated detection of hate speech as a binary task. However, the varied nature of Islamophobia means that this is often inappropriate for both theoretically informed social science and effective monitoring of social media platforms. Drawing on in-depth conceptual work we build an automated software tool which distinguishes between non-Islamophobic, weak Islamophobic and strong Islamophobic content. Accuracy is 77.6% and balanced accuracy is 83%. Our tool enables future quantitative research into the drivers, spread, prevalence and effects of Islamophobic hate speech on social media.

As recent perpetrators of racially-motivated mass-shootings were visitors and contributors to online white supremacist networks, this article presents a preliminary model that attempts to understand the social psychological effects of interacting in these networks. Combining Jonathan Turner’s theory of the social psychological roots of extreme violence, scholarship on the white supremacist movement, and research on computer-mediated communication, it suggests that interactions in online white supremacist networks accelerate the conversion from secondary to tertiary deviance, amplify the negative emotional energy released by this conversion, and, under certain conditions, transform this energy into a violent kinetic force. The model is not unique to online white supremacist networks and will hopefully guide research in other types of networks as well.

Between 2014 and 2017, the Islamic State maintained vibrant communities on a range of social media platforms. Due to aggressive account and content takedown policies by the major platforms, these visible communities are now almost non-existent. Following the March 2019 Christchurch attack, the question as to why major platforms cannot rout the extreme right in the same way has repeatedly arisen. In this article, Maura Conway explores why this is not as straightforward as it may seem.

Over the last decade, there has been an increased focus among researchers on the role of the Internet among actors and groups across the political and ideological spectrum. There has been particular emphasis on the ways that far-right extremists utilize forums and social media to express ideological beliefs through sites affiliated with real-world extremist groups and unaffiliated websites. The majority of research has used qualitative assessments or quantitative analyses of keywords to assess the extent of specific messages. Few have considered the breadth of extremist ideologies expressed among participants so as to quantify the proportion of beliefs espoused by participants. This study addressed this gap in the literature through a content analysis of over 18,000 posts from eight far-right extremist forums operating online. The findings demonstrated that the most prevalent ideological sentiments expressed in users’ posts involved anti-minority comments, though they represent a small proportion of all posts made in the sample. Additionally, users expressed associations to far-right extremist ideologies through their usernames, signatures, and images associated with their accounts. The implications of this analysis for policy and practice to disrupt extremist movements were discussed in detail.

This article represents an initial exploration of the content and posting strategies of the current wave of racist flyer drops in the US, focusing specifically on a dataset of all documented flyers posted in 2018. The dataset was generated by the Anti-Defamation League (ADL) and augmented by the authors. The dataset is unique among open sources and includes a large number of incidents which were not reported in the media. The article consists of three parts. The first documents and briefly discusses the groups engaged in racist flyer development and drops. The second describes and characterises the text and image content of the flyers. The final section uses open sources and leaked material to describe the process by which flyer drops are instigated, planned and documented.