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Critical assessments of the recent resurgence of right-wing nationalism have rightly highlighted the role of social media in these troubling times, yet they are constrained by an overemphasis on celebrity leaders defined as populists. This article departs from a leader-centric analysis and the liberal frame that still largely informs the assessment of political action, to foreground “fun” as a salient aspect of right-wing mobilization. Building on ethnographic fieldwork among the Hindu nationalists in India, I argue that fun is a meta practice that shapes the interlinked practices of fact-checking, abuse, assembly and aggression among online volunteers for the right-wing movement. Furthermore, fun remains crucial for an experience of absolute autonomy among online users in ideological battles. Providing the daily drip feed for exclusion, fun as a meta practice bears a formal similarity to objectivity in its performative effects of distance and deniability.

VOX-Pol Blog Post.

VOX-Pol Blog post.

Anashid – Islamic a cappella songs – figure prominently in the propagandaconsumption habits of the 17 jihadists who have caused casualties in the United States since 2013.

In this paper we present a multimodal approach to categorizing user posts based on their discussion topic. To integrate heterogeneous information extracted from the posts, i.e. text, visual content and the information about user interactions with the online platform, we deploy graph convolutional networks that were recently proven effective in classification tasks on knowledge graphs. As the case study we use the analysis of violent online political extremism content, a challenging task due to a particularly high semantic level at which extremist ideas are discussed. Here we demonstrate the potential of using neural networks on graphs for classifying multimedia content and, perhaps more importantly, the effectiveness of multimedia analysis techniques in aiding the domain experts performing qualitative data analysis. Our conclusions are supported by extensive experiments on a large collection of extremist posts. This research was produced with the aid of VOX-Pol Research Mobility Programme funding and supervision by VOX-Pol colleagues at Dublin City University.

Social media platforms play a key role in fostering the outreach of extremism by influencing the views, opinions, and perceptions of people. These platforms are increasingly exploited by extremist elements for spreading propaganda, radicalizing, and recruiting youth. Hence, research on extremism detection on social media platforms is essential to curb its influence and ill effects. A study of existing literature on extremism detection reveals that it is restricted to a specific ideology, binary classification with limited insights on extremism text, and manual data validation methods to check data quality. In existing research studies, researchers have used datasets limited to a single ideology. As a result, they face serious issues such as class imbalance, limited insights with class labels, and a lack of automated data validation methods. A major contribution of this work is a balanced extremism text dataset, versatile with multiple ideologies verified by robust data validation methods for classifying extremism text into popular extremism types such as propaganda, radicalization, and recruitment. The presented extremism text dataset is a generalization of multiple ideologies such as the standard ISIS dataset, GAB White Supremacist dataset, and recent Twitter tweets on ISIS and white supremacist ideology. The dataset is analyzed to extract features for the three focused classes in extremism with TF‐IDF unigram, bigrams, and trigrams features. Additionally, pretrained word2vec features are used for semantic analysis. The extracted features in the proposed dataset are evaluated using machine learning classification algorithms such as multinomial Naïve Bayes, support vector machine, random forest, and XGBoost algorithms. The best results were achieved by support vector machine using the TF‐IDF unigram model confirming 0.67 F1 score. The proposed multi‐ideology and multiclass dataset shows comparable performance to the existing datasets limited to single ideology and binary labels.