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After a period of self‐regulation, countries around the world began to implement regulations for the removal of terrorist content from tech platforms. However, much of this regulation has been criticised for a variety of reasons, most prominently for concerns of infringing free speech and creating unfair burdens for smaller platforms. In addition to this, regulation is heavily centred around content moderation, however, fails to consider or address the psychosocial risks it poses to human content moderators. This paper argues that where regulation has been heavily criticised yet continues to inspire similar regulation a new regulatory approach is required. The aim of this paper is to undertake an introductory examination of the use of a social regulation approach in three other industries (environmental protection, consumer protection and occupational health and safety) to learn and investigate new regulatory avenues that could be applied to the development of new regulation that seeks to counter terrorist content on tech platforms and is concerned with the safety of content moderators.

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.

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.

The advent of social media has led to an increased concern over its potential to propagate hate speech and misinformation, which, in addition to contributing to prejudice and discrimination, has been suspected of playing a role in increasing social violence and crimes in the United States. While literature has shown the existence of an association between posting hate speech and misinformation online and certain personality traits of posters, the general relationship and relevance of online hate speech/misinformation in the context of overall psychological wellbeing of posters remain elusive. One difficulty lies in the lack of adequate data analytics tools capable of adequately analyzing the massive amount of social media posts to uncover the underlying hidden links. Recent progresses in machine learning and large language models such as ChatGPT have made such an analysis possible. In this study, we collected thousands of posts from carefully selected communities on the social media site Reddit. We then utilized OpenAI’s GPT3 to derive embeddings of these posts, which are high-dimensional real-numbered vectors that presumably represent the hidden semantics of posts. We then performed various machine-learning classifications based on these embeddings in order to understand the role of hate speech/misinformation in various communities. Finally, a topological data analysis (TDA) was applied to the embeddings to obtain a visual map connecting online hate speech, misinformation, various psychiatric disorders, and general mental health.