Online Library

This study tests whether social media disinformation contributes to domestic terrorism within countries. I theorize that disinformation disseminated by political actors online through social media heightens political polarization within countries and that this, in turn, produces an environment where domestic terrorism is more likely to occur. I test this theory using data from more than 150 countries for the period 2000–2017. I find that propagation of disinformation through social media drives domestic terrorism. Using mediation tests I also verify that disinformation disseminated through social media increases domestic terrorism by, among other processes, enhancing political polarization within society.

The counterterrorism and CVE community has long questioned the effectiveness of counterspeech in countering extremism online. While most evaluation of counterspeech rely on limited reach and engagement metrics, this paper explores two models to better measure behavioral change and sentiment analysis. Conducted via partnerships between Facebook and counter-extremism NGOs, the first model uses A/B testing to analyze the effects of counterspeech exposure on low-prevalence-high-risk audiences engaging with Islamist extremist terrorist content. The second model builds upon online safety intervention approaches and the Redirect Method through a search based “get-help” module, redirecting white-supremacy and Neo-Nazi related search-terms to disengagement NGOs.

Several alternative social media platforms have emerged in response to perceptions that mainstream platforms are censoring traditional conservative ideologies. However, many of these alternative social media platforms have evolved to be outlets for hate speech and violent extremism. This study examines hate-based channels on Telegram from a US perspective. While Telegram has often been studied in relation to ISIS, less is known about its usage by US extremist users and movements. The authors used OSINT and observational methods on a sample of 125 Telegram channels containing hate speech and violent extremist content from far-right and far-left perspectives. The authors hypothesized that there would be a greater and growing presence of far-right activity compared to farleft activity due to current migration trends away from mainstream social media by the far-right. The authors also sought to observe the presence of disinformation campaigns, conspiracy theories, and accelerationism on Telegram. This study had four major findings: (1) the findings supported the hypothesis that more channels were host to farright dialogues, yet there were several far-left channels present, (2) 64.8% of the channels grew in size over a oneweek period, (3) 47 of the 125 channels were connected to well-known violent extremist movements or hate groups, and (4) QAnon and the COVID-19 pandemic were the most prominent sources of disinformation and conspiracy theories on Telegram. The findings of this study highlight that alternative social media platforms are a growing environment for a range of hateful ideologies and are aiding the spread of disinformation campaigns. This study concludes with a discussion on future strategies to combat the influence of the Internet on radicalization outcomes.

In February 2019, Moonshot launched The Redirect Method with funding from the Community Resilience Fund and in collaboration with the Canada Centre for Community Engagement and
Violence Prevention at Public Safety Canada. Canada Redirect was first deployed across all 13 Canadian provinces and territories, and in June 2019 our campaigns were subdivided to incorporate 353 postcodes in Canada’s six largest cities. These localized campaigns enabled Moonshot to collect granular data on extremist search appetite, test experimental messaging, and
explore the viability of providing at-risk users with content and services in their community.

Using the updated Redirect Method, Canada Redirect aimed to reach at-risk users with content that aligned as closely as possible with what they were searching for. Moonshot used subject-matter expertise and in-house knowledge to match relevant counter-narratives to their respective target audiences. This approach taps into a range of content ecosystems, such as music, gaming, and literature, to deliver alternative messaging that contrasts with the extremist content a user may be searching for, such as neo-Nazi manifestos or an ISIS nasheed. Moonshot adapted the Redirect Method to include content specific to the Canadian context in order to increase the relevance and impact of counter-narratives to the Canadian at-risk audience.

This report details Canada Redirect’s project phases, achievements, and findings from our digital campaigns, which were deployed across Canada for over a year, from 21 February 2019 to 23 March 2020.

Social media platforms are popular for expressing personal views, emotions and beliefs. Social media platforms are influential for propagating extremist ideologies for group-building, fund-raising, and recruitment. To monitor and control the outreach of extremists on social media, detection of extremism in social media is necessary. The existing extremism detection literature on social media is limited by specific ideology, subjective validation methods, and binary or tertiary classification. A comprehensive and comparative survey of datasets, classification techniques, validation methods with online extremism detection tool is essential. The systematic literature review methodology (PRISMA) was used. Sixty-four studies on extremism research were collected, including 31 from SCOPUS, Web of Science (WoS), ACM, IEEE, and 33 thesis, technical and analytical reports using Snowballing technique. The survey highlights the role of social media in propagating online radicalization and the need for extremism detection on social media platforms. The review concludes lack of publicly available, class-balanced, and unbiased datasets for better detection and classification of social-media extremism. Lack of validation techniques to evaluate correctness and quality of custom data sets without human interventions, was found. The information retrieval unveiled that contemporary research work is prejudiced towards ISIS ideology. We investigated that deep learning based automated extremism detection techniques outperform other techniques. The review opens the research opportunities for developing an online, publicly available automated tool for extremism data collection and detection. The survey results in conceptualization of architecture for construction of multi-ideology extremism text dataset with robust data validation techniques for multiclass classification of extremism text.

This study involved the creation of factors and indicators that can detect radicalization in social media posts. A concurrent approach of an expert knowledge acquisition process (modified Delphi technique) and literature review was utilized. Seven Singapore subject-matter experts in the field of terrorism evaluated factors that were collated from six terrorism risk assessment tools (ERG 22+, IVP, TRAP-18, MLG, VERA-2, and Cyber-VERA). They identify those that are of most considerable relevance for detecting radicalization in social media posts. A parallel literature review on online radicalization was conducted to complement the findings from the expert panel. In doing so, 12 factors and their 42 observable indicators were derived. These factors and indicators have the potential to guide the development of cyber-focused screening tools to detect radicalization in social media posts.