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This article presents a comparative analysis of online communities of right-wing radicals and Islamists, who are considered to be numerous and dangerous extremist groups in Russian society. The online communities were selected based on the content posted on the largest Russian social networking site VKontakte. The goal of this article is to determine the strategy and tactics employed by extremist online communities for survival on social networking sites. The authors discovered that both right-wing radical and Islamist groups employ similar behavioural techniques, with the mimicry of ideologically neutral content as the most common. In addition, every extremist community also applies some unique methods. For example, if there is a risk of being blocked, right-wing radicals tend to shift their activity and communication to the other internet-based platforms that are not under state control; however, Islamists prefer to suddenly change the content of their communities (i.e. by using secondary mimicry).

By exploring lessons learned from Ethiopia and Finland, this article challenges two assumptions about online hate speech research. First, it challenges the assumption that the best way to understand controversial concepts such as online hate speech is to determine how closely they represent or mirror some underlying set of facts or state of affairs online or in social media. Second, it challenges the assumption that academic research should be seen as separate from the many controversies that surround online hate speech debates globally. In its place, the article proposes the theory of “commentary” as a comparative research framework aimed at explaining how the messy and complex world of online and social media practices is articulated as hate speech over other ways of imagining this growing problem in global digital media environments.

This paper compares the social media posts of ISIS foreign fighters to those of ISIS supporters. We examine a random sample of social media posts made by violent foreign fighters (n = 14; 2000 posts) and non-violent supporters (n = 18; 2000 posts) of the Islamic State of Iraq and Syria (ISIS) (overall n = 4,000 posts), from 2009 to 2015. We used a mixed-method study design. Our qualitative content analyses of the 4,000 posts identified five themes: Threats to in-group, societal grievances, pursuit for significance, religion, and commitment issues. Our quantitative comparisons found that the dominant themes in the foreign fighters’ online content were threats to in-group, societal grievances, and pursuit for significance, while religion and commitment issues were dominant themes in the supporters’ online content. We also identified thematic variations reflecting individual attitudes that emerged during the 2011–2015 period, when major geopolitical developments occurred in Syria and Iraq. Finally, our quantitative sentiment-based analysis found that the supporters (10 out of 18; 56%) posted more radical content than the foreign fighters (5 out of 14; 36%) on social media.

This paper uses a computer-based frequency analysis to present an ideological discourse analysis of extremist ideologies in Daesh discourse. More specifically, by using a computer-assisted text analysis, the paper attempts to investigate the hidden extremist ideologies beyond the discourse of the first issue of Rumiyah, one of the main digital publications of Daesh. The paper’s main objectives are to expose hidden ideologies beyond the mere linguistic form of discourse, to offer better linguistic understanding of the manipulative use of language in religious discourse, and to highlight the relevance of using a computer-based frequency analysis to discourse studies and corpus linguistics. The paper also employs van Dijk’s ideological discourse analysis, by adopting his positive self-presentation and negative other-presentation strategies. Findings reveal that Daesh discourse in Rumiyah is rhetorically structured to hide the manipulative ideologies of its users, which in turn functions to reformulate the social, political and religious attitudes of its readers.

The Internet has made it possible for people to access just about any information they could possibly want. Conversely, it has given organizations a vehicle through which they can get their message out to a large audience. Hate groups have found the Internet particularly appealing, because they are able to get their uncensored message out to an unlimited number of people (ADL 2005). This is an issue that is not likely to go away.

Proliferation of social media platforms in recent past has resulted into upsurge in the number of users. Advent of these sites have paved way for the users to easily express share and communicate. In such a scenario, it is imperative to analyze the content and identify nasty content so as to avoid unpleasant situations. Machine learning techniques are extensively used for this purpose. In this paper, we propose a language model for the identification of hate speech in twitter data. Distil-BERT, a context aware embedding model along with Support Vector Machine (SVM) for the classification of hate speech has been used. SVM with a 10-fold cross validation and linear kernel has been found to provide better accuracy as compared to existing models. Results show that accuracy is improved with the use of context aware embedding model.