# Deep Learning Models for Multilingual Hate Speech Detection

arXiv: https://arxiv.org/abs/2004.06465 · HTML: https://mlsanity.com/paper/2004.06465/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2020.

## Built on by (most cited first, 30 of 49)

- [A Holistic Approach to Undesired Content Detection in the Real World](https://mlsanity.com/paper/2208.03274/) (2022)
- [What's in the Box? A Preliminary Analysis of Undesirable Content in the Common Crawl Corpus](https://mlsanity.com/paper/2105.02732/) (2021)
- [Is Your Toxicity My Toxicity? Exploring the Impact of Rater Identity on Toxicity Annotation](https://mlsanity.com/paper/2205.00501/) (2022)
- [Language-Driven Image Style Transfer](https://mlsanity.com/paper/2106.00178/) (2021)
- [Tell Me What Happened: Unifying Text-guided Video Completion via Multimodal Masked Video Generation](https://mlsanity.com/paper/2211.12824/) (2022)
- [A systematic review of Hate Speech automatic detection using Natural Language Processing](https://mlsanity.com/paper/2106.00742/) (2021)
- [Hate and Offensive Speech Detection in Hindi and Marathi](https://mlsanity.com/paper/2110.12200/) (2021)
- [ToxiCraft: A Novel Framework for Synthetic Generation of Harmful Information](https://mlsanity.com/paper/2409.14740/) (2024)
- [MultiParaDetox: Extending Text Detoxification with Parallel Data to New Languages](https://mlsanity.com/paper/2404.02037/) (2024)
- ["Short is the Road that Leads from Fear to Hate": Fear Speech in Indian WhatsApp Groups](https://mlsanity.com/paper/2102.03870/) (2021)
- [HateDay: Insights from a Global Hate Speech Dataset Representative of a Day on Twitter](https://mlsanity.com/paper/2411.15462/) (2024)
- [Lex Rosetta: Transfer of Predictive Models Across Languages, Jurisdictions, and Legal Domains](https://mlsanity.com/paper/2112.07882/) (2021)
- [BanTH: A Multi-label Hate Speech Detection Dataset for Transliterated Bangla](https://mlsanity.com/paper/2410.13281/) (2024)
- [Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning Based Methods](https://mlsanity.com/paper/2105.14875/) (2021)
- [One to rule them all: Towards Joint Indic Language Hate Speech Detection](https://mlsanity.com/paper/2109.13711/) (2021)
- [IIITT@LT-EDI-EACL2021-Hope Speech Detection: There is always Hope in Transformers](https://mlsanity.com/paper/2104.09066/) (2021)
- [Abusive and Threatening Language Detection in Urdu using Boosting based and BERT based models: A Comparative Approach](https://mlsanity.com/paper/2111.14830/) (2021)
- [BLUE at Memotion 2.0 2022: You have my Image, my Text and my Transformer](https://mlsanity.com/paper/2202.07543/) (2022)
- [Guardians of Discourse: Evaluating LLMs on Multilingual Offensive Language Detection](https://mlsanity.com/paper/2410.15623/) (2024)
- [ToxiLab: How Well Do Open-Source LLMs Generate Synthetic Toxicity Data?](https://mlsanity.com/paper/2411.15175/) (2024)
- [MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMs](https://mlsanity.com/paper/2505.19800/) (2025)
- [Cross-lingual hate speech detection based on multilingual domain-specific word embeddings](https://mlsanity.com/paper/2104.14728/) (2021)
- [Which one is more toxic? Findings from Jigsaw Rate Severity of Toxic Comments](https://mlsanity.com/paper/2206.13284/) (2022)
- [Manifestations of Xenophobia in AI Systems](https://mlsanity.com/paper/2212.07877/) (2022)
- [Hate Speech and Offensive Content Detection in Indo-Aryan Languages: A Battle of LSTM and Transformers](https://mlsanity.com/paper/2312.05671/) (2023)
- [Muted: Multilingual Targeted Offensive Speech Identification and Visualization](https://mlsanity.com/paper/2312.11344/) (2023)
- [Exploring the Adversarial Capabilities of Large Language Models](https://mlsanity.com/paper/2402.09132/) (2024)
- [Highly Generalizable Models for Multilingual Hate Speech Detection](https://mlsanity.com/paper/2201.11294/) (2022)
- [A Review of Challenges in Machine Learning based Automated Hate Speech Detection](https://mlsanity.com/paper/2209.05294/) (2022)
- [Human-in-the-Loop Hate Speech Classification in a Multilingual Context](https://mlsanity.com/paper/2212.02108/) (2022)

## Abstract (arXiv)

Hate speech detection is a challenging problem with most of the datasets available in only one language: English. In this paper, we conduct a large scale analysis of multilingual hate speech in 9 languages from 16 different sources. We observe that in low resource setting, simple models such as LASER embedding with logistic regression performs the best, while in high resource setting BERT based models perform better. In case of zero-shot classification, languages such as Italian and Portuguese achieve good results. Our proposed framework could be used as an efficient solution for low-resource languages. These models could also act as good baselines for future multilingual hate speech detection tasks. We have made our code and experimental settings public for other researchers at https://github.com/punyajoy/DE-LIMIT.