# Down the Rabbit Hole: Detecting Online Extremism, Radicalisation, and Politicised Hate Speech

arXiv: https://arxiv.org/abs/2301.11579 · HTML: https://mlsanity.com/paper/2301.11579/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 160 references that are in the map.
- First posted on arXiv 27 Jan 2023.

## Builds on (most cited first, 26 of 26)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Big Bird: Transformers for Longer Sequences](https://mlsanity.com/paper/2007.14062/) (2020)
- [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://mlsanity.com/paper/1908.03557/) (2019)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://mlsanity.com/paper/2204.06745/) (2022)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes](https://mlsanity.com/paper/2005.04790/) (2020)
- [Predicting the Type and Target of Offensive Posts in Social Media](https://mlsanity.com/paper/1902.09666/) (2019)
- [SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)](https://mlsanity.com/paper/1903.08983/) (2019)
- [Hate Speech Dataset from a White Supremacy Forum](https://mlsanity.com/paper/1809.04444/) (2018)
- [Deep Learning for Hate Speech Detection in Tweets](https://mlsanity.com/paper/1706.00188/) (2017)
- [Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT model](https://mlsanity.com/paper/2008.06460/) (2020)
- [Aggression-annotated Corpus of Hindi-English Code-mixed Data](https://mlsanity.com/paper/1803.09402/) (2018)
- [Algorithmic Amplification of Politics on Twitter](https://mlsanity.com/paper/2110.11010/) (2021)
- [Detecting Offensive Language in Tweets Using Deep Learning](https://mlsanity.com/paper/1801.04433/) (2018)
- [Towards Hate Speech Detection at Large via Deep Generative Modeling](https://mlsanity.com/paper/2005.06370/) (2020)
- [UM-IU@LING at SemEval-2019 Task 6: Identifying Offensive Tweets Using BERT and SVMs](https://mlsanity.com/paper/1904.03450/) (2019)
- [Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twitter](https://mlsanity.com/paper/1905.08067/) (2019)
- [Online reactions to the 2017 'Unite the Right' rally in Charlottesville: Measuring polarization in Twitter networks using media followership](https://mlsanity.com/paper/1905.07755/) (2019)
- [Applying Social Media Intelligence for Predicting and Identifying On-line Radicalization and Civil Unrest Oriented Threats](https://mlsanity.com/paper/1511.06858/) (2015)

## Built on by (most cited first, 5 of 5)

- [Improving code-mixed hate detection by native sample mixing: A case study for Hindi-English code-mixed scenario](https://mlsanity.com/paper/2405.20755/) (2024)
- [Time Series Analysis of Key Societal Events as Reflected in Complex Social Media Data Streams](https://mlsanity.com/paper/2403.07090/) (2024)
- [AggregHate: An Efficient Aggregative Approach for the Detection of Hatemongers on Social Platforms](https://mlsanity.com/paper/2409.14464/) (2024)
- [Social Hatred: Efficient Multimodal Detection of Hatemongers](https://mlsanity.com/paper/2506.19603/) (2025)
- [Introducing the Privacy-HSD Trade-off: Hate Speech Detection, but not at the Cost of Privacy](https://mlsanity.com/paper/2608.19006/) (2026)

## Abstract (arXiv)

Social media is a modern person's digital voice to project and engage with new ideas and mobilise communities unicodex2013 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.