Online Library

This report aims to provide a first look into Irish far-right activity on the messaging app, Telegram, where the movement is operating both as identifiable groups and influencers, and anonymously-run channels and groups.

The report looks at the activity across 34 such Telegram channels through the lens of a series of case studies where content posted on these channels resulted in real life consequences. Using both quantitative and qualitative methods, the report examines the tactics, language and trends within these channels, providing much-needed detail on the activity of the Irish far-right online.

Hate speech is a growing concern online, with minorities and vulnerable groups increasingly targeted with extreme denigration and hostility. Why users express hate speech on social media is unclear. This study explores how this hate speech develops on both mainstream and fringe social media platforms; Facebook and Gab. We investigate whether users seek out hostile areas of these platforms in order to express hate, or whether users develop these opinions through a mechanism of socialisation, as they interact with others over time. We find evidence that some users do arrive on these platforms with pre-existing hate stances, while others develop them with time spent on the platform. We find that hate speech is unevenly distributed, with a small number of users contributing a large proportion of the hate on the platforms. Our analysis reveals how hate speech develops online, the important role of the group environment in accelerating its development and gives insight into the development of counter measures.

This paper observes some of the most distinguished characteristics of the Islamic State related to the use of modern technology and tries to drawn some important conclusions between the terrorist’s quasi state, modernity and technology. After the examination of the functioning of IS at the peak of its powers between 2014 and 2017, the analysis turns to terrorists’ various online activities. All of them are showing the Islamic State’s reliance on modern technology, especially IT, as one of the most important aspects of its terrorist activities that greatly contributed not only to the effectiveness, but to the essential definition of first modern terrorist quasi-state. The second part of the paper deals with the Islamic State`s fully reliance on technology in its own legitimization (both among Islamist rivals and “infidels”). The celebration and the fascination with modern technology as main IS characteristics make it different from other Islamist terrorist groups, and trying to establish relations between modernity and terrorism based on religious fundamentalism. The paper also tries to find answers to the question whether IS’s ultra-modern techno approach is responsible for its transformation from a classical fundamentalist terrorist group into some kind of modern political ideology and a social movement with totalitarian and murderous characteristics.

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.

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.

While the radical right is relatively successful on social media, only a fraction of its supporters become activists offline. Why do some individuals become offline activists in radical right organizations, while others prefer to limit their support to online spaces? Following an inductive analysis of interviews with activists and supporters of a radical right organization, this study points to the importance of going beyond classical explanations for activism by focusing on the impact of, and relationship between, group identification and stigma for mobilization. It shows that activists who identify with the organization and consider it stigmatized are motivated to participate in protest actions to speak out in front of this perceived injustice. By contrast, supporters who question the organization’s legitimacy in the face of stigma engage with the organization on social media and are motivated by a desire to keep a low profile and to avoid being openly associated with stigmatized actors.