# Highly Generalizable Models for Multilingual Hate Speech Detection

arXiv: https://arxiv.org/abs/2201.11294 · HTML: https://mlsanity.com/paper/2201.11294/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 27 Jan 2022.

## Builds on (most cited first, 10 of 10)

- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior](https://mlsanity.com/paper/1802.00393/) (2018)
- [Hate Speech Dataset from a White Supremacy Forum](https://mlsanity.com/paper/1809.04444/) (2018)
- [Multilingual and Multi-Aspect Hate Speech Analysis](https://mlsanity.com/paper/1908.11049/) (2019)
- [A Survey on Data Augmentation for Text Classification](https://mlsanity.com/paper/2107.03158/) (2021)
- [Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis](https://mlsanity.com/paper/1701.08118/) (2017)
- [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)
- [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)

## Built on by (most cited first, 3 of 3)

- [On the Calibration of Massively Multilingual Language Models](https://mlsanity.com/paper/2210.12265/) (2022)
- [Human-in-the-Loop Hate Speech Classification in a Multilingual Context](https://mlsanity.com/paper/2212.02108/) (2022)
- [English offensive text detection using CNN based Bi-GRU model](https://mlsanity.com/paper/2409.15652/) (2024)

## Abstract (arXiv)

Hate speech detection has become an important research topic within the past decade. More private corporations are needing to regulate user generated content on different platforms across the globe. In this paper, we introduce a study of multilingual hate speech classification. We compile a dataset of 11 languages and resolve different taxonomies by analyzing the combined data with binary labels: hate speech or not hate speech. Defining hate speech in a single way across different languages and datasets may erase cultural nuances to the definition, therefore, we utilize language agnostic embeddings provided by LASER and MUSE in order to develop models that can use a generalized definition of hate speech across datasets. Furthermore, we evaluate prior state of the art methodologies for hate speech detection under our expanded dataset. We conduct three types of experiments for a binary hate speech classification task: Multilingual-Train Monolingual-Test, MonolingualTrain Monolingual-Test and Language-Family-Train Monolingual Test scenarios to see if performance increases for each language due to learning more from other language data.