Abstract
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.
Abstract (translated)
社交媒体是现代人的数字化声音,用于发布和参与新思想,团结社区,与极端主义者分享一种力量。考虑到未审查的内容过滤算法对于极端主义、 radicalization 和仇恨言论(ERH)的检测所带来的社会风险,负责任的软件工程必须理解谁、什么、何时、何地以及为什么这种模型是必要的,以保护用户安全和自由表达。因此,我们提出了并研究 ERH 上下文挖掘的独特研究领域,以统一分散的研究。具体来说,我们评估了从社会和技术定义建立和数据集收集策略到技术算法设计和性能的整个设计过程。我们的2015-2021年51项研究系统性文献综述(SLR)提供了文本、网络和视觉方法用于检测极端主义关联、仇恨内容以及针对团体和运动的极端主义化。我们识别了以共识驱动的 ERH 定义,并提出了解决现有的意识形态和地理偏见的解决方案,特别是由于大洋洲和澳大利亚的研究缺乏。我们的混合研究对自然语言处理、社区检测和视觉文本模型展示了文本 transformer-based 算法的主导地位。我们的结论是为 ERH 上下文挖掘研究人员提供的重要建议,并提出了接受指南的路线图,以启用更安全的网络空间。
URL
https://arxiv.org/abs/2301.11579