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.

研究 ERH 上下文挖掘的特殊领域，汇总了 51 项相关研究，着重于自然语言处理、社区检测和视觉文本模型，提出了推荐给研究人员、政府和行业的实施路线图，旨在建立更安全的网络空间。