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Through existing literature and open-source materials – including academic articles, research reports, policy documents, newspaper articles, investigative journalism, government inquiries and previous relevant Radicalisation Awareness Network (RAN) Policy Support (PS) deliverables, etc. – this paper will investigate the following key questions: what is gamification of (violent) extremism, what are the current and future threats it presents to the European Union (EU), and how can it be countered?

The paper explores the role of social media on positive youth development and engagement towards peacebuilding in Pakistan. In particular, it recognizes the role of youth within the liberal peace process, hence, sustainably aiding peacebuilding efforts. The paper argues that within developing democracies, media and youth can create an environment to promote dialogue and collaborative problem-solving techniques. Furthermore, it explicates how extreme ideologists exploit social media; hence, negatively influencing the youth. With regards, the paper discusses several aspects of social media that can significantly contribute towards countering violent extremism and related narratives. Such an understanding enables us to classify the potential role of social media in involving youth in contextualized peacebuilding efforts.

This paper maps communication channels exploited by the Salafi-jihadist violent extremist organisations (VEOs) and their followers between March 2020 and June 2022 on The Onion Router (TOR). It argues that the true scale of digital jihadist presence on TOR has remained insignificant for years. Militant Islamists have mostly used .onion domains as backup propaganda dissemination channels, which enable content takedown policies introduced by countering violent extremism stakeholders to be circumvented. Aside from propaganda distribution, TOR attracts Salafi-jihadist VEOs and their followers for other reasons, as it facilitates anonymous communication, crowdfunding, sharing of terrorist manuals or the organisation of terrorist attacks.

Since the beginning of 2020, while societies and economies around the world have struggled to cope with the realities of the COVID-19 pandemic, cyberspace has given governments, businesses, and general end-users the ability to work, play, and connect in new and innovative ways. With everything from workspaces and classrooms to family gatherings and exercise routines forced online, the Internet has enabled people across the globe to carry on and maintain a sense of normalcy during very abnormal times. However, at the same time, while the world has been focused on the health, economic, political, and social ramifications of the pandemic, terrorist organizations, fringe groups, and extremist communities around the world have become emboldened, finding opportunity to exploit the situation, incite hate, (re)mobilize, and promote their ideologies online in novel ways. These groups—which we loosely classify as malicious nonstate actors for the purposes of this research —have been primarily focused on exploiting and contributing to the diffusion of information during the pandemic for their own strategic gain.

Ephemeral digital trace data can decrease the completeness, reproducibility, and reliability of social media datasets. Systematic post deletions thus potentially bias the results of computational methods used to map actors, content, and online information diffusion. Therefore, the aim of this study was to assess the extent and distribution of message deletion across different data types using data from the hybrid messenger service Telegram, which has experienced an influx of deplatformed users from mainstream social media platforms. A repeatedly scraped sample of messages from public Telegram groups and channels was used to investigate the effect of message ephemerality on the consistency of Telegram datasets. The findings revealed that message deletion introduces biases to the computational collection and analysis of Telegram data. Further, message ephemerality reduces dataset consistency, the quality of social network analyses, and the results of computational content analysis methods, such as topic modeling or dictionaries. The implications of these findings for scholars aiming to use Telegram data for computational research, possible solutions, and contributions to the methodological advancement of studying online political communication are discussed further in this article.

This thesis aimed to address these limitations by developing a cross-platform analysis framework for online-hate researchers to gain a clearer understanding of the dynamics of the global hate ecosystem. More specifically, the designing of this framework involved examining the main functionalities of existing online-hate analysis frameworks, and the extent to which they address cross-platform hate. The strengths and limitations of these approaches then informed the functional requirements of the cross-platform analysis framework. To demonstrate how the framework can provide novel insights into online-hate research, this thesis also details its application to various case studies, including online hate from white supremacy-supporting users and environments spread during the 2020 US election and the COVID-19 pandemic.