# Tackling Hate Speech in Low-resource Languages with Context Experts

arXiv: https://arxiv.org/abs/2303.16828 · HTML: https://mlsanity.com/paper/2303.16828/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2023.

## Built on by (most cited first, 7 of 7)

- [Lost in Translation: Large Language Models in Non-English Content Analysis](https://mlsanity.com/paper/2306.07377/) (2023)
- [A Material Lens on Coloniality in NLP](https://mlsanity.com/paper/2311.08391/) (2023)
- [FASIONAD : FAst and Slow FusION Thinking Systems for Human-Like Autonomous Driving with Adaptive Feedback](https://mlsanity.com/paper/2411.18013/) (2024)
- [NaijaHate: Evaluating Hate Speech Detection on Nigerian Twitter Using Representative Data](https://mlsanity.com/paper/2403.19260/) (2024)
- [Hate Speech Detection in Limited Data Contexts using Synthetic Data Generation](https://mlsanity.com/paper/2310.02876/) (2023)
- [ViHateT5: Enhancing Hate Speech Detection in Vietnamese With A Unified Text-to-Text Transformer Model](https://mlsanity.com/paper/2405.14141/) (2024)
- [A Survey on Automatic Online Hate Speech Detection in Low-Resource Languages](https://mlsanity.com/paper/2411.19017/) (2024)

## Abstract (arXiv)

Given Myanmars historical and socio-political context, hate speech spread on social media has escalated into offline unrest and violence. This paper presents findings from our remote study on the automatic detection of hate speech online in Myanmar. We argue that effectively addressing this problem will require community-based approaches that combine the knowledge of context experts with machine learning tools that can analyze the vast amount of data produced. To this end, we develop a systematic process to facilitate this collaboration covering key aspects of data collection, annotation, and model validation strategies. We highlight challenges in this area stemming from small and imbalanced datasets, the need to balance non-glamorous data work and stakeholder priorities, and closed data-sharing practices. Stemming from these findings, we discuss avenues for further work in developing and deploying hate speech detection systems for low-resource languages.