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This article offers a panoramic vision of the studies carried out on the Manosphere and anti-feminism in the Spanish and overarching European context. In this work, we have reviewed the most recent and relevant studies focused on understanding organized online misogyny and toxic technocultures. In addition, the incipient works on the Spanish manosphere developed in the last five years have been analyzed in order to connect European knowledge with local advances. This critical review offers a state of the art that includes: a definition of the manosphere, a defense of the importance of its study, a review of the topics covered by subject areas, a diagnosis of what remains to be done, and an ethical warning for those researchers who want to commit to the work of unraveling what the manosphere is.

This article examines algorithmic content moderation, using the moderation of violent extremist content as a specific case. In recent years, algorithms have increasingly been mobilized to perform essential moderation functions for online social media platforms such as Facebook, YouTube, and Twitter, including limiting the proliferation of extremist speech. Drawing on Katherine Hayles’ concept of “cognitive assemblages” and the Critical Security Studies literature, we show how algorithmic regulation operates within larger assemblages of humans and non-humans to influence the surveillance and regulation of information flows. We argue that the dynamics of algorithmic regulation are more liquid, cobbled together and distributed than it appears. It is characterized by a set of shifting human and machine entities, which mix traditional surveillance methods with more sophisticated tools, and whose linkages and interactions are transient. The processes that enable the consolidation of knowledge about risky profiles and contents are, therefore, collective and distributed among humans and machines. This allows us to argue that the cognitive assemblages involved in content moderation become a cobbled space of preemptive calculation.

Social media and terrorist financing (SMTF) refers to how terror organisations (TO), sometimes called violent extremist organisations (VEOs), have exploited free social media platforms to raise money for terrorism/fighting/jihad, to recruit, train, equip, and transport fighters, to support extremist religious proto-states (e.g., Islamic State of Iraq and Syria [ISIS]), to market/brand an organisation including to disseminate its ideologies, and/or to amass wealth. This chapter considers the scope of SMTF and legal/policy responses to its ever-evolving practices, discusses how it is practised and by whom, and considers the challenges of fighting SMTF. SMTF is part of the larger concept of cyberterrorism where terrorists target computer systems maliciously and use digital technology to facilitate terror attacks, though some argue that these two actions are completely different phenomena. The chapter concludes by summarising the key points of the discussion, offering suggestions for future study of this phenomenon, and reflecting on the societal impacts of SMTF.

It has become a familiar observation that social media usage can afford material assistance to the advancement of violent extremist or terrorist causes and can be a tool for counter-extremism and counter-terrorism efforts. This chapter discusses how several core supporting pillars of extremism mesh with social media in apparently similar ways to non-extremist activity and then considers the limited available literature on non-extremist online fundraising, moving thereafter to the literature which is more focused specifically on the study of social media funding for extremist or terrorist causes. It highlights the potential role of symbolic representation of shared values on social media and how this can help to address the challenges of an era where large-scale but ostensibly non-extremist movements such as QAnon and Stop the Steal arguably share some values in common with extremist causes and hence are a potential funding reservoir not necessarily observed if one focuses on specific extremist messaging content or extremist symbology.

The nature of terrorism financing has evolved with the exponential development and expansion of the internet. This dimension of terrorism funding has become a critical source for terrorist organisational mobilisation. The cyber funding of terrorism has coincided with the remarkable technological progress that has been achieved in the last few decades. This chapter provides a general background to the development of cyber terrorism funding. Then, it introduces relevant areas surrounding the cyber dimensions of terrorism financing that are currently challenging scholars, experts, and policymakers alike, including discussions on social media, cryptocurrencies, and the dark web. The chapter then discusses some technological tools and initiatives needed to counter the evolving nature of cyber terrorism funding through artificial intelligence techniques and protocols. Finally, it concludes with a discussion on future challenges. One potential tool that can aid in preventing terrorism funding is the utilisation of artificial intelligence.

Social media is a modern person’s digital voice to project and engage with new ideas and mobilise communities—a power shared with extremists. Given the societal risks of unvetted content-moderating algorithms for Extremism, Radicalisation, and Hate speech (ERH) detection, responsible software engineering must understand the who, what, when, where, and why such models are necessary to protect user safety and free expression. Hence, we propose and examine the unique research field of ERH context mining to unify disjoint studies. Specifically, we evaluate the start-to-finish design process from socio-technical definition-building and dataset collection strategies to technical algorithm design and performance. Our 2015–2021 51-study Systematic Literature Review (SLR) provides the first cross-examination of textual, network, and visual approaches to detecting extremist affiliation, hateful content, and radicalisation towards groups and movements. We identify consensus-driven ERH definitions and propose solutions to existing ideological and geographic biases, particularly due to the lack of research in Oceania/Australasia. Our hybridised investigation on Natural Language Processing, Community Detection, and visual-text models demonstrates the dominating performance of textual transformer-based algorithms. We conclude with vital recommendations for ERH context mining researchers and propose an uptake roadmap with guidelines for researchers, industries, and governments to enable a safer cyberspace.