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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 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.

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.

VOX-Pol Blog post.