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Although many law enforcement and intelligence agencies are concerned about online communities known to facilitate violent right-wing extremism, little is empirically known about the presence of extremist ideologies, expressed grievances, or violent mobilization efforts that make up these spaces. In this study, we conducted a content analysis of a sample of postings from two of the most conspicuous right-wing extremist forums known for facilitating violent extremism, Iron March and Fascist Forge. We identified a number of noteworthy posting patterns within and across forums that may assist law enforcement and intelligence agencies in identifying credible threats online.

Social media has become an essential part of the daily routines of children and adolescents. Moreover, enormous efforts have been made to ensure the psychological and emotional well-being of young users as well as their safety when interacting with various social media platforms. In this paper, we investigate the exposure of those users to inappropriate comments posted on YouTube videos targeting this demographic. We collected a large-scale dataset of approximately four million records, and studied the presence of five age-inappropriate categories and the amount of exposure to each category. Using natural language processing and machine learning techniques, we constructed ensemble classifiers that achieved high accuracy in detecting inappropriate comments. Our results show a large percentage of worrisome comments with inappropriate content: we found 11% of the comments on children’s videos to be toxic, highlighting the importance of monitoring comments, particularly on children platforms.

Despite the ongoing need for researchers, practitioners, and policymakers to identify and assess the online activities of violent extremists prior to their engagement in violence offline, little is empirically known about their online behaviors generally or differences in their posting behaviors compared to non-violent extremists who share similar ideological beliefs particularly. In this study, we drew from a unique sample of violent and non-violent right-wing extremists to compare their posting behaviors within a sub-forum of the largest white supremacy web-forum. Analyses for the current study proceeded in three phases. First, we plotted the average posting trajectory for users in the sample, followed by an assessment of the rates at which they stayed active or went dormant in the sub-forum. We then used logistic regression to examine whether specific posting behaviors were characteristic of users’ violence status. The results highlight a number of noteworthy differences in the posting behaviors of violent and non-violent right-wing extremists, many of which may inform future risk factor frameworks used by law enforcement and intelligence agencies to identify credible threats online. We conclude with a discussion of the implications of this analysis, its limitations and avenues for future research.

The growth of right-wing extremism, especially where it segues into hate crime and terrorism, poses new challenges for governments, not least because its perpetrators are typically lone actors, often radicalized online. The United Kingdom has struggled to define, tackle or legitimate against extremism, though it already has an extensive array of terrorism-related offences that target expression, encouragement, publication and possession of terrorist material. In 2019, the United Kingdom went further to make viewing terrorist-related material online on a single occasion a crime carrying a 15-year maximum sentence. This article considers whether UK responses to extremism, particularly those that target non-violent extremism, are necessary, proportionate, effective and compliant with fundamental rights. It explores whether criminalizing the curiosity of those who explore radical political ideas constitutes legitimate criminalization or overextends state power and risks chilling effects on freedom of speech, association, academic freedom, journalistic enquiry and informed public debate—all of which are the lifeblood of a liberal democracy.

This article uses the context of the widespread circulation of accounts about “CoronaJihad” in India during the COVID-19 pandemic to examine how public WhatsApp groups that participate in disseminating such accounts function within the ecosystem of hate around Hindutva majoritarianism in the country. The manner in which the WhatsApp platform operates within this ecosystem is mapped through a granular study of three public Hindutva WhatsApp groups; the messages within these groups during the first phase of the COVID-19 lock-down in India were examined during the course of this study. The pattern of messaging within the three groups that contribute to the narrative of “CoronaJihad,” which blames the minority Muslim community for the spread of the virus in India, were analyzed. The article focuses on factors including company policies and the specific sociopolitical situation in the country to understand the circumstances that make WhatsApp’s deep entanglement with the divisive politics of Hindutva majoritarianism in India possible.

WhatsApp is the most popular messaging app in the world. Due to its popularity, WhatsApp has become a powerful and cheap tool for political campaigning being widely used during the 2019 Indian general election, where it was used to connect to the voters on a large scale. Along with the campaigning, there have been reports that WhatsApp has also become a breeding ground for harmful speech against various protected groups and religious minorities. Many such messages attempt to instil fear among the population about a specific (minority) community. According to research on inter-group conflict, such `fear speech’ messages could have a lasting impact and might lead to real offline violence. In this paper, we perform the first large scale study on fear speech across thousands of public WhatsApp groups discussing politics in India. We curate a new dataset and try to characterize fear speech from this dataset. We observe that users writing fear speech messages use various events and symbols to create the illusion of fear among the reader about a target community. We build models to classify fear speech and observe that current state-of-the-art NLP models do not perform well at this task. Fear speech messages tend to spread faster and could potentially go undetected by classifiers built to detect traditional toxic speech due to their low toxic nature. Finally, using a novel methodology to target users with Facebook ads, we conduct a survey among the users of these WhatsApp groups to understand the types of users who consume and share fear speech. We believe that this work opens up new research questions that are very different from tackling hate speech which the research community has been traditionally involved in.