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Crafted as an open communication platform characterized by high anonymity and minimal moderation, Telegram has garnered increasing popularity among activists operating within repressive political contexts, as well as among political extremists and conspiracy theorists. While Telegram offers valuable data access to research non-institutionalized activism, scholars studying the latter on Telegram face unique theoretical and methodological challenges in systematically defining, selecting, sampling, and classifying relevant actors and content. This literature review addresses these issues by considering a wide range of recent research. In particular, it discusses the methodological challenges of sampling and classifying heterogeneous groups of (often non-institutionalized) actors. Drawing on social movement research, we first identify challenges specific to the characteristics of non-institutionalized actors and how they become interlaced with Telegram’s platform infrastructure and requirements. We then discuss strategies from previous Telegram research for the identification and sampling of a study population through multistage sampling procedures and the classification of actors. Finally, we derive challenges and potential strategies for future research and discuss ethical challenges.

In recent decades, political parties espousing extreme nationalist, xenophobic, and even outright racist platforms have enjoyed variable success in national elections across Europe. While vibrant research literature has sought to better understand the sources of support for such parties, remarkably little attention has been paid to the interplay between parties and the broader social networks of extremism in which they are embedded. To remedy this deficiency, the present study examines the relations between far-right parliamentary parties and their extra-parliamentary networks. One level of analysis tests whether there is a relationship between a party’s position within a network and its sustainability. Social network analysis is employed to assess the nature and structure of ties between Belgian organizations online. In addition, systematic textual analysis of website content is used to determine how a party ideological position within the network impacts its sustainability. The second level of analysis is a qualitative study based on in-depth interviews with members of the Flemish nationalist organization in order to better understand how actors experience social networks. Evidence suggests that the most sustainable parties are those that have dense connections with other nationalist organizations. Mapping relations between far-right parties that compete openly within the rules of institutionalized democracy and their wider social networks can provide important policy-relevant insight into contemporary challenges posed by illiberal forces.

In the domain of computer science, the last years have witnessed the improvement of social network analysis at scale. One of the most challenging aspects of social network analysis is community detection; analysts use a variety of tools to visualise the spontaneous group structure emerging from interactions and friendship relations in multi-million-user networks. This visualisation, combined with influencer detection and automated text analysis tools such as topic detection, enables the analyst to grasp most of the complexity of a social network. This computer-science-oriented study explores three lines of research concerning online extremism. First, about the emerging narratives and the topics that can be found on open platforms. We show that many actors actively use terror-group related terms; most cannot be directly tied to any specific organisation. A second axis concerns the connections between platforms: the information space has no central point as content is shared across platforms. However, the links reveal clusters of locations: we observe a group of Pakistan-India conflict mentions, and a cluster of US alt-right websites, transforming terrorism into a migration problem. The third axis relates to the social media landscape structure. We rely on a combination of document-level topic modelling and graph analysis to detect and explore the social data, visualising the types of groups that are active on the topic. Among the results, we found a small botnet circulating a pro-Daesh pamphlet and a set of grassroot reactions that managed to moderate a controversial pro-Jihadi post on Reddit.

Measuring extremist behavior and discourse online presents a significant methodological challenge largely due to the volume of content. This paper assesses the extent to which discourse communities created by extremists identified through qualitative analysis in prior research are present in a novel Telegram dataset and can be identified through Latent Dirichlet Allocation topic modeling and network analysis. It then compares the results of Louvian and Girvan-Newman network community detection methods and the topics assigned to each community to see if the underlying structure of association between topics is robust to the use of different community detection methods. The results indicate that it may be possible to map discourse communities through topic modeling and network analysis. However, the comparison of the algorithms is inconclusive. This work contributes to our understanding of how computational social science methods can be used to measure and analyze extremist use of the Internet at scale.

This study explores the internal dynamics and networks of terrorist groups in cyberspace—in particular, Al-Qaeda and the Islamic State of Iraq and al-Sham (ISIS). Using a “Global Cyberterrorism Dataset” that features data on cyberterror attacks between 2011 and 2016, this research analyzes these two terrorist groups through the lens of a cyber-conflict theory that integrates conflict theory with Jaishankar’s space transition theory. Through a network analysis methodology, we examine the invisible relationships and connections between the national origins and target countries of cyberterror attacks. The analysis focuses on the networks of national origins of terrorists and victims, network structures of Al-Qaeda and ISIS actors, and clustering networks of Al-Qaeda and ISIS cyberterrorists. Results indicate that terror in cyberspace is ubiquitous, more flexible than traditional terrorism, and that cyberattacks mostly occurred within the countries of origin. We conclude by discussing the complex features of cyberterror networks and identify some of the geostrategic implications of the divergent cyber strategies adopted by Al-Qaeda and ISIS.

The project Mapping Networks and Narratives of Online Right-Wing Extremists in New South Wales (NSW) used the systematic mining and analysis of online data to generate evidence-based insights into online right-wing extremism (RWE) across the state. The project was conducted between July 2019 and February 2020 with data collection occurring from August to November 2019. The project addressed three key areas:

– What is the nature of the online RWE environment in NSW?
– How are themes and narratives framed in different online contexts in order to mobilise support?
– What level of risk does the online RWE environment pose?

The research areas were framed as broad questions to facilitate wide exploratory research into the online RWE movement in NSW, a milieu that has been little studied. This breadth of scope was considered pertinent in the wake of the March 2019 mass casualty terrorist attack in Christchurch, New Zealand, by an attacker originating from NSW.