# Multilingual Hate Speech Detection and Counterspeech Generation: A Comprehensive Survey and Practical Guide

arXiv: https://arxiv.org/abs/2603.19279 · HTML: https://mlsanity.com/paper/2603.19279/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 94 references that are in the map.
- First posted on arXiv 1 Mar 2026.

## Builds on (most cited first, 30 of 37)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [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)
- [JudgeLM: Fine-tuned Large Language Models are Scalable Judges](https://mlsanity.com/paper/2310.17631/) (2023)
- [Predicting the Type and Target of Offensive Posts in Social Media](https://mlsanity.com/paper/1902.09666/) (2019)
- [Latent Hatred: A Benchmark for Understanding Implicit Hate Speech](https://mlsanity.com/paper/2109.05322/) (2021)
- [Hate Speech Dataset from a White Supremacy Forum](https://mlsanity.com/paper/1809.04444/) (2018)
- [Directions in Abusive Language Training Data: Garbage In, Garbage Out](https://mlsanity.com/paper/2004.01670/) (2020)
- [Multilingual and Multi-Aspect Hate Speech Analysis](https://mlsanity.com/paper/1908.11049/) (2019)
- [CONAN -- COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech](https://mlsanity.com/paper/1910.03270/) (2019)
- [Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speech](https://mlsanity.com/paper/2107.08720/) (2021)
- [Thou shalt not hate: Countering Online Hate Speech](https://mlsanity.com/paper/1808.04409/) (2018)
- [Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models](https://mlsanity.com/paper/2206.09917/) (2022)
- [Deep Learning Models for Multilingual Hate Speech Detection](https://mlsanity.com/paper/2004.06465/) (2020)
- [A Multimodal Framework for the Detection of Hateful Memes](https://mlsanity.com/paper/2012.12871/) (2020)
- [Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition](https://mlsanity.com/paper/2002.10361/) (2020)
- [Aggression-annotated Corpus of Hindi-English Code-mixed Data](https://mlsanity.com/paper/1803.09402/) (2018)
- [Detecting Harmful Content On Online Platforms: What Platforms Need Vs. Where Research Efforts Go](https://mlsanity.com/paper/2103.00153/) (2021)
- [Hostility Detection Dataset in Hindi](https://mlsanity.com/paper/2011.03588/) (2020)
- [NLP for Counterspeech against Hate: A Survey and How-To Guide](https://mlsanity.com/paper/2403.20103/) (2024)
- [On Zero-Shot Counterspeech Generation by LLMs](https://mlsanity.com/paper/2403.14938/) (2024)
- [HateCheckHIn: Evaluating Hindi Hate Speech Detection Models](https://mlsanity.com/paper/2205.00328/) (2022)
- [Intent-conditioned and Non-toxic Counterspeech Generation using Multi-Task Instruction Tuning with RLAIF](https://mlsanity.com/paper/2403.10088/) (2024)
- [Outcome-Constrained Large Language Models for Countering Hate Speech](https://mlsanity.com/paper/2403.17146/) (2024)
- [Leveraging Multilingual Transformers for Hate Speech Detection](https://mlsanity.com/paper/2101.03207/) (2021)
- [Multilingual Counter Narrative Type Classification](https://mlsanity.com/paper/2109.13664/) (2021)
- [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)
- [Deciphering Implicit Hate: Evaluating Automated Detection Algorithms for Multimodal Hate](https://mlsanity.com/paper/2106.05903/) (2021)
- [Uncovering Political Hate Speech During Indian Election Campaign: A New Low-Resource Dataset and Baselines](https://mlsanity.com/paper/2306.14764/) (2023)

## Built on by (most cited first, 1 of 1)

- [When Surface Form Changes Moderation Decisions: A Paired Study of Code-Mixed Workflow Instability](https://mlsanity.com/paper/2606.05654/) (2026)

## Abstract (arXiv)

Combating online hate speech in multilingual settings requires approaches that go beyond English-centric models and capture the cultural and linguistic diversity of global online discourse. This paper presents a comprehensive survey and practical guide to multilingual hate speech detection and counterspeech generation, integrating recent advances in natural language processing. We analyze why monolingual systems often fail in non-English and code-mixed contexts, missing implicit hate and culturally specific expressions. To address these challenges, we outline a structured three-phase framework - task design, data curation, and evaluation - drawing on state-of-the-art datasets, models, and metrics. The survey consolidates progress in multilingual resources and techniques while highlighting persistent obstacles, including data scarcity in low-resource languages, fairness and bias in system development, and the need for multimodal solutions. By bridging technical progress with ethical and cultural considerations, we provide researchers, practitioners, and policymakers with scalable guidelines for building context-aware, inclusive systems. Our roadmap contributes to advancing online safety through fairer, more effective detection and counterspeech generation across diverse linguistic environments.