Data-Efficient Strategies for Expanding Hate Speech Detection into Under-Resourced Languages
Open accessAbstract
Hate speech is a global phenomenon, but most hate speech datasets so far focus on Englishlanguage content.This hinders the development of more effective hate speech detection models in hundreds of languages spoken by billions across the world.More data is needed, but annotating hateful content is expensive, timeconsuming and potentially harmful to annotators.To mitigate these issues, we explore dataefficient strategies for expanding hate speech detection into under-resourced languages.In a series of experiments with mono-and multilingual models across five non-English languages, we find that 1) a small amount of target-language fine-tuning data is needed to achieve strong performance, 2) the benefits of using more such data decrease exponentially, and 3) initial fine-tuning on readily-available English data can partially substitute targetlanguage data and improve model generalisability.Based on these findings, we formulate actionable recommendations for hate speech detection in low-resource language settings.
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A small volume of data was used to train models for the classification of both cyberbullying and non-cyberbullying text.
“Based on the utilization of a small dataset for training learning models to detect cyberbullying on social media platforms, the review identified that a small volume of data was used to train models for the classification of both cyberbullying and non-cyberbullying text [147,148].”
From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [147]
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- Authors
- Röttger, P., Nozza, D., Bianchi, F., & Hovy, D.
- Affiliations
- University of Oxford (GB)
- Bocconi University (IT)
- Stanford University (US)
- Venue
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Published
- 2022
- Pages
- 5674-5691
- OpenAlex
- W4385573219
- Cited by
- 18
- References
- 55
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 3 · 2023: 9 · 2022: 1
- FWCI
- 2.35
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- European Commission
- Fondazione Cariplo
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- hate speech detection, low-resource languages, data efficiency, fine-tuning, multilingual models, annotation cost, model generalization
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