Online Library

VOX-Pol Guest Lecture Series, Autumn 2022

Speaker: Prof Maura Conway

Title: Online extremism and terrorism research: Where we’ve been, where we’re going

Outline: As part of the inaugural guest lecture for the 2022 series, Prof Maura Conway discusses “Online extremism and terrorism research: Where we’ve been, where we’re going”. The lecture is preceded by an introduction by VOX-Pol co-ordinator Prof Stuart Macdonald and is followed by a Q&A moderated by Seán Looney.

VOX-Pol Guest Lecture Series, Autumn 2022

Speaker: J.M. Berger

Title: The Conceptualisation of Extremism Online

The Counter-Terrorism Committee unanimously adopted the Delhi Declaration at the closing of its special meeting on countering the use of new and emerging technologies for terrorist purposes, held in India on 28-29 October 2022.

The special meeting focused on three main areas: unmanned aerial systems, terrorist financing and information and communications technologies. The meeting also examined overarching issues relating to the use of technologies for terrorist purposes, to include human rights, gender considerations, and future programming by the international community. The Delhi Declaration provides non-binding guidance to Member States and stakeholders in countering terrorist use of new and emerging technologies.

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.