Online Library

This is a formal commentary, responding to Matthew Curran Benigni, Kenneth Joseph, and Kathleen Carley’s contribution, “Online extremism and the communities that sustain it: Detecting the ISIS supporting community on Twitter”. This brief review reflects on the ethics of big data research methodologies, and how novel methods complicate long-standing principles of research ethics. Specifically, the concept of the “data subject” as a corollary, or replacement, of “human subject” is considered.

VOX-Pol Blog post.

In this study, we examine key psychological dimensions in the manifestos authored by the perpetrators of the Christchurch and Utøya massacres, the right-wing extremists Brenton Tarrant and Anders Breivik, and the ISIS propaganda magazine Rumiyah. All texts were authored and disseminated virtually with the purpose of attracting or consolidating support, and justifying violent, discriminatory actions. While right-wing supremacist and extremist Islamist discourses are ostensibly ideologically opposed, previous research has posited the existence of ideational and emotive commonalities. We approach this from a corpus-linguistic perspective, and employ the software LIWC2015 and Wmatrix to explore the dominant psychological dimensions, semantic categories and keywords in these texts. We identify elements that contribute to the construction of a narrative of hate, peril and urgency, and discuss differences in the imagery used to construct these meanings and to appeal to different audiences. Whilst our analysis supports the existence of commonalities in ideological content and discursive strategies, our results identify differences in the target of hate in right-wing supremacist discourse and we differentiate between primarily Islamophobic and racist motives. Finally, we also discuss the limitations inherent in employing these software tools to analyse discourse in the Web 2.0 era.

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.

Every day, internet users encounter hateful and dangerous speech online, and some of them choose to respond directly in order to refute or undermine it. We call this counterspeech. Only a few studies have attempted to measure the effectiveness of counterspeech directly, and as far as we know, this is the first review of relevant literature.

We’ve collected and reviewed related articles from a range of fields including political science, sociology, countering violent extremism, and computational social science. These articles do not all use the term “counterspeech,” but they shed light on various features of successful counterspeech, for example, qualities that make speakers/authors more influential in online interactions or the extent to which pro- and anti-social behavior is contagious on the internet.

Recent advances in generative Artificial Intelligence (AI) have inspired interest in using it to counter online hatred. This updated version of the annotated bibliography contains an entirely new section reviewing recent research related to AI-based counterspeech tools.

#jagärhar is by far the largest and best-organized collective effort to respond directly to hatred online, anywhere in the world, as far as we know. It is also one of only two civil society efforts against hatred online to have been replicated in numerous other countries. In this detailed account of its efforts– the first qualitative study of such a group – Cathy Buerger shares her findings on how and why #jagärhär members do what they do, how working collectively influences members’ ability and willingness to respond to hatred, and how the group’s strategy is carefully designed to take advantage of Facebook’s algorithms and influence ideas and discourse norms among the general public – not necessarily the ones writing the hateful comments.