Social media is a modern person's digital voice to project and engage with new ideas and mobilise communities $\unicode{x2013}$ 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.

翻译：社交媒体是现代人投射、参与新思想并动员社区的数字声音——这一力量也与极端主义者共享。鉴于未经验证的内容审核算法在检测极端主义、激进化与仇恨言论（ERH）方面带来的社会风险，负责任的软件工程必须理解这些模型保护用户安全与言论自由所需的“谁、什么、何时、何处、为何”。因此，我们提出并审视独特的ERH语境挖掘研究领域，以整合分散的研究工作。具体而言，我们评估从社会技术定义构建和数据集收集策略到技术算法设计与性能的全流程设计过程。我们对2015-2021年间51项研究进行的系统性文献综述（SLR），首次交叉检验了文本、网络与视觉方法在检测极端主义关联、仇恨内容及针对群体与运动的激进化方面的应用。我们识别出基于共识的ERH定义，并提出解决现有意识形态与地理偏见的方案，尤其是大洋洲/澳大拉西亚地区研究不足所导致的问题。我们针对自然语言处理、社区检测及视觉-文本模型的混合研究，展示了基于文本的Transformer算法的主导性能。最后，我们为ERH语境挖掘研究者提出重要建议，并制定面向研究者、行业与政府采用路线图的指导方针，以构建更安全的网络空间。