# A systematic review of Hate Speech automatic detection using Natural Language Processing

arXiv: https://arxiv.org/abs/2106.00742 · HTML: https://mlsanity.com/paper/2106.00742/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 53 of its 159 references that are in the map.
- First posted on arXiv 22 May 2021.

## Builds on (most cited first, 30 of 53)

- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection](https://mlsanity.com/paper/2012.10289/) (2020)
- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [Predicting the Type and Target of Offensive Posts in Social Media](https://mlsanity.com/paper/1902.09666/) (2019)
- [Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior](https://mlsanity.com/paper/1802.00393/) (2018)
- [Ex Machina: Personal Attacks Seen at Scale](https://mlsanity.com/paper/1610.08914/) (2016)
- [SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)](https://mlsanity.com/paper/1903.08983/) (2019)
- [HateBERT: Retraining BERT for Abusive Language Detection in English](https://mlsanity.com/paper/2010.12472/) (2020)
- [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)
- [Text Classification Algorithms: A Survey](https://mlsanity.com/paper/1904.08067/) (2019)
- [FlauBERT: Unsupervised Language Model Pre-training for French](https://mlsanity.com/paper/1912.05372/) (2019)
- [SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)](https://mlsanity.com/paper/2006.07235/) (2020)
- [Multilingual and Multi-Aspect Hate Speech Analysis](https://mlsanity.com/paper/1908.11049/) (2019)
- [A Benchmark Dataset for Learning to Intervene in Online Hate Speech](https://mlsanity.com/paper/1909.04251/) (2019)
- [CONAN -- COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech](https://mlsanity.com/paper/1910.03270/) (2019)
- [Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language](https://mlsanity.com/paper/1905.07213/) (2019)
- [BERTje: A Dutch BERT Model](https://mlsanity.com/paper/1912.09582/) (2019)
- [Exploring Hate Speech Detection in Multimodal Publications](https://mlsanity.com/paper/1910.03814/) (2019)
- [Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis](https://mlsanity.com/paper/1701.08118/) (2017)
- [Comparative Study of CNN and RNN for Natural Language Processing](https://mlsanity.com/paper/1702.01923/) (2017)
- [Portuguese Named Entity Recognition using BERT-CRF](https://mlsanity.com/paper/1909.10649/) (2019)
- [Detecting Online Hate Speech Using Context Aware Models](https://mlsanity.com/paper/1710.07395/) (2017)
- [Characterizing and Detecting Hateful Users on Twitter](https://mlsanity.com/paper/1803.08977/) (2018)
- [Offensive Language Identification in Greek](https://mlsanity.com/paper/2003.07459/) (2020)
- [Offensive Language and Hate Speech Detection for Danish](https://mlsanity.com/paper/1908.04531/) (2019)
- [Deep Learning Models for Multilingual Hate Speech Detection](https://mlsanity.com/paper/2004.06465/) (2020)

## Built on by (most cited first, 27 of 27)

- [COLD: A Benchmark for Chinese Offensive Language Detection](https://mlsanity.com/paper/2201.06025/) (2022)
- [On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark](https://mlsanity.com/paper/2110.08466/) (2021)
- [The Unseen Targets of Hate -- A Systematic Review of Hateful Communication Datasets](https://mlsanity.com/paper/2405.08562/) (2024)
- [DriftGuard: Safety-Aware Multi-Monitor Detection and Selective Adaptation for Evolving Toxicity Moderation](https://mlsanity.com/paper/2606.28725/) (2026)
- [Digital Guardians: Can GPT-4, Perspective API, and Moderation API reliably detect hate speech in reader comments of German online newspapers?](https://mlsanity.com/paper/2501.01256/) (2025)
- [Moderating Harm: Benchmarking Large Language Models for Cyberbullying Detection in YouTube Comments](https://mlsanity.com/paper/2505.18927/) (2025)
- [A Survey of Machine Learning Models and Datasets for the Multi-label Classification of Textual Hate Speech in English](https://mlsanity.com/paper/2504.08609/) (2025)
- [Do You Really Need a GPU to Guard Your LLM? CPU-Class Classifiers and Multi-Stage Pipelines for Safety Enforcement at Scale](https://mlsanity.com/paper/2512.19011/) (2025)
- [Hate Speech Detection Using Cross-Platform Social Media Data In English and German Language](https://mlsanity.com/paper/2410.05287/) (2024)
- [HP-BERT: A framework for longitudinal study of Hinduphobia on social media via language models](https://mlsanity.com/paper/2501.05482/) (2025)
- [EdgeAIGuard: Agentic LLMs for Minor Protection in Digital Spaces](https://mlsanity.com/paper/2503.00092/) (2025)
- [Combating Toxic Language: A Review of LLM-Based Strategies for Software Engineering](https://mlsanity.com/paper/2504.15439/) (2025)
- [Tuning-Free Structured Sparse PCA via Deep Unfolding Networks](https://mlsanity.com/paper/2502.20837/) (2025)
- [Stronger Together: Unleashing the Social Impact of Hate Speech Research](https://mlsanity.com/paper/2505.13251/) (2025)
- [Understanding and Analyzing Inappropriately Targeting Language in Online Discourse: A Comparative Annotation Study](https://mlsanity.com/paper/2505.16847/) (2025)
- [Bias-Aware Mislabeling Detection via Decoupled Confident Learning](https://mlsanity.com/paper/2507.07216/) (2025)
- [Detoxify: A framework for abusive text transformation using LLMs](https://mlsanity.com/paper/2507.10177/) (2025)
- [Before the Outrage: Challenges and Advances in Predicting Online Antisocial Behavior](https://mlsanity.com/paper/2507.20614/) (2025)
- [Defining, Understanding, and Detecting Online Toxicity: Challenges and Machine Learning Approaches](https://mlsanity.com/paper/2509.14264/) (2025)
- [Boosting Accuracy and Interpretability in Multilingual Hate Speech Detection Through Layer Freezing and Explainable AI](https://mlsanity.com/paper/2601.02697/) (2026)
- [LLMs-Integrated Automatic Hate Speech Recognition Using Controllable Text Generation Models](https://mlsanity.com/paper/2601.04654/) (2026)
- [Large language models for spreading dynamics in complex systems](https://mlsanity.com/paper/2602.08085/) (2026)
- [The geometry of online conversations and the causal antecedents of conflictual discourse](https://mlsanity.com/paper/2602.15600/) (2026)
- [ToxiREX: A Dataset on Toxic REasoning in ConteXt](https://mlsanity.com/paper/2606.27981/) (2026)
- [Hate Speech Detection in Turkish and Arabic: A Comprehensive Study](https://mlsanity.com/paper/2607.00143/) (2026)
- [EvoHarmBench: Breaking Content Moderation with Iterative Human-Like Evasion](https://mlsanity.com/paper/2608.27844/) (2026)
- [An Explainable System for Offensive Content Detection in Nastaliq Urdu: Corpus Curation, Transformer Benchmarking, and Word-Level Attribution](https://mlsanity.com/paper/2610.15795/) (2026)

## Abstract (arXiv)

With the multiplication of social media platforms, which offer anonymity, easy access and online community formation, and online debate, the issue of hate speech detection and tracking becomes a growing challenge to society, individual, policy-makers and researchers. Despite efforts for leveraging automatic techniques for automatic detection and monitoring, their performances are still far from satisfactory, which constantly calls for future research on the issue. This paper provides a systematic review of literature in this field, with a focus on natural language processing and deep learning technologies, highlighting the terminology, processing pipeline, core methods employed, with a focal point on deep learning architecture. From a methodological perspective, we adopt PRISMA guideline of systematic review of the last 10 years literature from ACM Digital Library and Google Scholar. In the sequel, existing surveys, limitations, and future research directions are extensively discussed.