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The online presence of incels, or involuntary celibates, has been an increasing security concern for researchers, practitioners, and policymakers in recent years, given that self-identified incels – including Alek Minassian and Elliot Rodger – used the Internet to disseminate incel ideology and manifestos prior to committing acts of violence. However, little is empirically known about the incel movement in general or their online communities in particular. The present study draws from a set of comments from r/Incels, a now defunct but once popular subreddit dedicated to the incel community, and compares the most highly-upvoted comments (n = 500) to a random set of other comments (n = 500) in the subreddit. This qualitative analysis focuses on identifying subcultural discourse that is widely supported and engaged with by members of the online community and the extent to which incels utilize this online space to reaffirm deviant behavior. Our study underscores the importance, as well as the difficulties, of drawing from online sources like web-forums to generate new knowledge on deviant communities and behaviors. We conclude with a discussion of the implications of this analysis, its limitations, and avenues for future research.

Hate has been a growing concern with hate-groups and individuals using the Internet, or more specifically, social media platforms, to globalize hate. Since these social media platforms can connect users around the world, hate-organizations are using these connections as opportunities to recruit candidates and spread their propaganda. Without opposing views, these extreme viewpoints can establish themselves as legitimate and then be used to incite hate in individuals. Thus, these extreme viewpoints must be countered by similar messages to discourage this online hate, and one such way is to use the same platforms through grassroots movements. This paper presents a case study which was conducted on a class of Criminology students who implemented a grassroots community-based campaign called Unity Starts with U (USwithU) to counter-hate in a community by using social media platforms to spread messages of inclusion and share experiences. The results from the campaign showed improvements on people’s attitude towards hate at the local community level. Based on literature and this campaign, policy recommendations are suggested for policymakers to consider when creating or making improvements on counter-narrative programs.

This study examines videos produced by the al-Hayat Media Center, a branch of the Islamic State’s (IS) larger media campaign aimed more specifically at Western audiences. Using a thematic analysis approach, recurring themes of 10 al-Hayat videos were identified with conclusions made regarding the specificities of the message and the target audience. It was found that al-Hayat videos cater to potential Western recruits and sympathizers by portraying life in the IS as spiritually and existentially fulfilling, while simultaneously decrying the West as secular, immoral, and criminal. By utilizing well-produced propaganda videos that tap into the dissatisfactions of Western Muslims, al-Hayat was shown to deliver a sophisticated and legitimate message that may play a role in the larger radicalization process.

This study applies the method of sentiment analysis to the online media released by the Islamic State (IS) in order to distinguish the ways in which IS uses language within their media, and potential ways in which this language differs across various online platforms. The data used for this sentiment analysis consist of transcripts of IS-produced videos, the text of IS-produced online periodical magazines, and social media posts from IS-affiliated Twitter accounts. It was found that the language and discourse utilised by IS in their online media is of a predominantly negative nature, with the language of videos containing the highest concentration of negative sentiment. The words and phrases with the most extreme sentiment values are used as a starting point for the identification of specific narratives that exist within online IS media. The dominant narratives discovered with the aid of sentiment analysis were: 1) the demonstrated strength of the IS, 2) the humiliation of IS enemies, 3) continuous victory, and 4) religious righteousness. Beyond the identification of IS narratives, this study serves to further explore the utility of the sentiment analysis method by applying it to mediums and data that it has not traditionally been applied to, specifically, videos and magazines.

The theoretical literature from criminology, social movements, and political sociology, among others, includes diverging views about how political outcomes could affect movements. Many theories argue that political defeats motivate the losing side to increase their mobilization while other established models claim the winning side may feel encouraged and thus increase their mobilization. We examine these diverging perspectives in the context of the extreme right online and recent presidential elections by measuring the effect of the 2008 and 2016 election victories of Obama and Trump on the volume of postings on the largest white supremacy web-forum. ARIMA time series using intervention modeling showed a significant and sizable increase in the total number of posts and right-wing extremist posts but no significant change for firearm posts in either election year. However, the volume of postings for all impact measures was highest for the 2008 election.

As violent extremists continue to surface in online discussion forums, law enforcement agencies search for new ways of uncovering their digital indicators. Researchers have both described and hypothesized a number of ways to detect online traces of potential extremists, yet this area of inquiry remains in its infancy. This study proposes a new search method that, through the analysis of sentiment, identifies the most radical users within online forums. Although this method is applicable to web-forums of any type, the method was evaluated on four Islamic forums containing approximately 1 million posts of its 26,000 unique users. Several characteristics of each user’s postings were examined, including their posting behavior and the content of their posts. The content was analyzed using Parts-Of-Speech tagging, sentiment analysis, and a novel algorithm called ‘Sentiment-based Identification of Radical Authors’, which accounts for a user’s percentile score for average sentiment score, volume of negative posts, severity of negative posts, and duration of negative posts. The results suggest that there is no simple typology that best describes radical users online; however, the method is flexible enough to evaluate several properties of a user’s online activity that can identify radical users on the forums.