# Towards generalisable hate speech detection: a review on obstacles and solutions

arXiv: https://arxiv.org/abs/2102.08886 · HTML: https://mlsanity.com/paper/2102.08886/

## Key facts

- Cited by 66 papers in the map (data through 14 Oct 2026).
- Builds on 57 of its 143 references that are in the map.
- First posted on arXiv 17 Feb 2021.

## Builds on (most cited first, 30 of 57)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Word Translation Without Parallel Data](https://mlsanity.com/paper/1710.04087/) (2017)
- [Social Bias Frames: Reasoning about Social and Power Implications of Language](https://mlsanity.com/paper/1911.03891/) (2019)
- [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)
- [Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems](https://mlsanity.com/paper/1805.04508/) (2018)
- [Ex Machina: Personal Attacks Seen at Scale](https://mlsanity.com/paper/1610.08914/) (2016)
- [Racial Bias in Hate Speech and Abusive Language Detection Datasets](https://mlsanity.com/paper/1905.12516/) (2019)
- [Advances in Pre-Training Distributed Word Representations](https://mlsanity.com/paper/1712.09405/) (2017)
- [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)
- [Demographic Dialectal Variation in Social Media: A Case Study of African-American English](https://mlsanity.com/paper/1608.08868/) (2016)
- [Deep Learning for Hate Speech Detection in Tweets](https://mlsanity.com/paper/1706.00188/) (2017)
- [Reducing Gender Bias in Abusive Language Detection](https://mlsanity.com/paper/1808.07231/) (2018)
- [SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)](https://mlsanity.com/paper/2006.07235/) (2020)
- [Understanding Abuse: A Typology of Abusive Language Detection Subtasks](https://mlsanity.com/paper/1705.09899/) (2017)
- [CONAN -- COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech](https://mlsanity.com/paper/1910.03270/) (2019)
- [A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media](https://mlsanity.com/paper/1910.12574/) (2019)
- [Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT model](https://mlsanity.com/paper/2008.06460/) (2020)
- [Contextualizing Hate Speech Classifiers with Post-hoc Explanation](https://mlsanity.com/paper/2005.02439/) (2020)
- [Detecting Online Hate Speech Using Context Aware Models](https://mlsanity.com/paper/1710.07395/) (2017)
- [Racial Disparity in Natural Language Processing: A Case Study of Social Media African-American English](https://mlsanity.com/paper/1707.00061/) (2017)

## Built on by (most cited first, 30 of 66)

- [A Holistic Approach to Undesired Content Detection in the Real World](https://mlsanity.com/paper/2208.03274/) (2022)
- [A New Generation of Perspective API: Efficient Multilingual Character-level Transformers](https://mlsanity.com/paper/2202.11176/) (2022)
- [Having Beer after Prayer? Measuring Cultural Bias in Large Language Models](https://mlsanity.com/paper/2305.14456/) (2023)
- [Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset](https://mlsanity.com/paper/2207.00220/) (2022)
- [Handling Bias in Toxic Speech Detection: A Survey](https://mlsanity.com/paper/2202.00126/) (2022)
- [When Do Annotator Demographics Matter? Measuring the Influence of Annotator Demographics with the POPQUORN Dataset](https://mlsanity.com/paper/2306.06826/) (2023)
- [Assessing Language Model Deployment with Risk Cards](https://mlsanity.com/paper/2303.18190/) (2023)
- [Interpretable Unified Language Checking](https://mlsanity.com/paper/2304.03728/) (2023)
- [How Hate Speech Varies by Target Identity: A Computational Analysis](https://mlsanity.com/paper/2210.10839/) (2022)
- [Overview of the HASOC Subtrack at FIRE 2021: Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages](https://mlsanity.com/paper/2112.09301/) (2021)
- [Understanding Counterspeech for Online Harm Mitigation](https://mlsanity.com/paper/2307.04761/) (2023)
- [Improving Generalizability in Implicitly Abusive Language Detection with Concept Activation Vectors](https://mlsanity.com/paper/2204.02261/) (2022)
- [Deep Learning for Hate Speech Detection: A Comparative Study](https://mlsanity.com/paper/2202.09517/) (2022)
- [Lost in Moderation: How Commercial Content Moderation APIs Over- and Under-Moderate Group-Targeted Hate Speech and Linguistic Variations](https://mlsanity.com/paper/2503.01623/) (2025)
- [AnnoBERT: Effectively Representing Multiple Annotators' Label Choices to Improve Hate Speech Detection](https://mlsanity.com/paper/2212.10405/) (2022)
- [Session-based Cyberbullying Detection in Social Media: A Survey](https://mlsanity.com/paper/2207.10639/) (2022)
- [Generalizing Hate Speech Detection Using Multi-Task Learning: A Case Study of Political Public Figures](https://mlsanity.com/paper/2208.10598/) (2022)
- [Multilingual Auxiliary Tasks Training: Bridging the Gap between Languages for Zero-Shot Transfer of Hate Speech Detection Models](https://mlsanity.com/paper/2210.13029/) (2022)
- [Detecting Harmful Online Conversational Content towards LGBTQIA+ Individuals](https://mlsanity.com/paper/2207.10032/) (2022)
- [Enhanced Multimodal Hate Video Detection via Channel-wise and Modality-wise Fusion](https://mlsanity.com/paper/2505.12051/) (2025)
- [CFL: Causally Fair Language Models Through Token-level Attribute Controlled Generation](https://mlsanity.com/paper/2306.00374/) (2023)
- [Offensive Language Identification in Low-resourced Code-mixed Dravidian languages using Pseudo-labeling](https://mlsanity.com/paper/2108.12177/) (2021)
- [Cyberbullying detection across social media platforms via platform-aware adversarial encoding](https://mlsanity.com/paper/2204.00334/) (2022)
- [Hidden behind the obvious: misleading keywords and implicitly abusive language on social media](https://mlsanity.com/paper/2205.01374/) (2022)
- [Antisemitic Messages? A Guide to High-Quality Annotation and a Labeled Dataset of Tweets](https://mlsanity.com/paper/2304.14599/) (2023)
- [PEACE: Cross-Platform Hate Speech Detection- A Causality-guided Framework](https://mlsanity.com/paper/2306.08804/) (2023)
- [BAN-PL: a Novel Polish Dataset of Banned Harmful and Offensive Content from Wykop.pl web service](https://mlsanity.com/paper/2308.10592/) (2023)
- [Cross-lingual Capsule Network for Hate Speech Detection in Social Media](https://mlsanity.com/paper/2108.03089/) (2021)
- [Sexism Prediction in Spanish and English Tweets Using Monolingual and Multilingual BERT and Ensemble Models](https://mlsanity.com/paper/2111.04551/) (2021)
- ["Stop Asian Hate!" : Refining Detection of Anti-Asian Hate Speech During the COVID-19 Pandemic](https://mlsanity.com/paper/2112.02265/) (2021)

## Abstract (arXiv)

Hate speech is one type of harmful online content which directly attacks or promotes hate towards a group or an individual member based on their actual or perceived aspects of identity, such as ethnicity, religion, and sexual orientation. With online hate speech on the rise, its automatic detection as a natural language processing task is gaining increasing interest. However, it is only recently that it has been shown that existing models generalise poorly to unseen data. This survey paper attempts to summarise how generalisable existing hate speech detection models are, reason why hate speech models struggle to generalise, sums up existing attempts at addressing the main obstacles, and then proposes directions of future research to improve generalisation in hate speech detection.