https://github.com/franciellevargas/HateBR
HateBR is the first large-scale expert annotated dataset of Brazilian Instagram comments for hate speech and offensive language detection on the web and social media.
https://github.com/franciellevargas/HateBR
brazilian-portuguese dataset hatespeech-detection machine-learning natural-language-processing text-classification
Last synced: about 1 month ago
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HateBR is the first large-scale expert annotated dataset of Brazilian Instagram comments for hate speech and offensive language detection on the web and social media.
- Host: GitHub
- URL: https://github.com/franciellevargas/HateBR
- Owner: franciellevargas
- Created: 2021-03-27T14:34:49.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2024-10-15T23:57:00.000Z (8 months ago)
- Last Synced: 2024-10-17T11:09:03.361Z (8 months ago)
- Topics: brazilian-portuguese, dataset, hatespeech-detection, machine-learning, natural-language-processing, text-classification
- Homepage:
- Size: 2.85 MB
- Stars: 26
- Watchers: 3
- Forks: 5
- Open Issues: 1
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-nlpbr - Hate Speech BR
README
[](https://doi.org/10.5281/zenodo.10794024)
HateBR - A Benchmark Dataset for Offensive Language and Hate Speech in Brazilian Portuguese
HateBR is the first large-scale expert annotated dataset of Brazilian Instagram comments for abusive language detection on the web and social media. The HateBR was collected from Brazilian Instagram comments of politicians and manually annotated by specialists. It is composed of 7,000 documents annotated according to three different layers: a binary classification (offensive versus non-offensive comments), offensiveness-level (highly, moderately, and slightly offensive messages), and hate speech targets. Each comment was annotated by three different expert annotators and achieved high inter-annotator agreement. Furthermore, baseline experiments were implemented outperforming the current literature dataset baselines for the Portuguese language. We hope that the proposed expert annotated dataset may foster research on hate speech detection in the Natural Language Processing area.
***UPDATE***: HateBR and HateBRXplain new versions are available
This repository contains the corpus and the best models presented in the LREC's paper (see section "CITING / BIBTEX").
The following table describes in detail the classes:
### HateBR
| class | label | total |
|--------------|-------|--------|
| offensive | 1 | 3,500 |
| non-offensive | 0 | 3,500 |
| **Total** | | **7,000** |
### HateBRXplain
| class | label | rationales | total |
|--------------|-------|--------------------------------|--------|
| offensive | 1 | human-annotated rationales | 3,500 |
| non-offensive | 0 | null | 3,500 |
| **Total** | | | **7,000** |
In addition, we also provide baseline machine learning results for both tasks: offensive language and hate speech detection. The best-obtained models are available here in .pkl files. File names are organized as `[classification (offensive or hate)_representation (ngram or tfidf)_algorithms (nb, svm, mlp or lr)]`. For example, the file *offensive_tfidf_svm.pkl* presents the model of offensive detection with tf-idf representation using the support vector machine algorithm.
CITING / BIBTEX
@inproceedings{vargas-etal-2022-hatebr,
title = "{H}ate{BR}: A Large Expert Annotated Corpus of {B}razilian {I}nstagram Comments for Offensive Language and Hate Speech Detection",
author = "Vargas, Francielle and
Carvalho, Isabelle and
Rodrigues de G{\'o}es, Fabiana and
Pardo, Thiago and
Benevenuto, Fabr{\'\i}cio",
booktitle = "Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022)",
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.777",
pages = "7174--7183",
}
@article{Vargas_Carvalho_Pardo_Benevenuto_2024,
author={Vargas, Francielle and Carvalho, Isabelle and Pardo, Thiago A. S. and Benevenuto, Fabrício},
title={Context-aware and expert data resources for Brazilian Portuguese hate speech detection},
DOI={10.1017/nlp.2024.18},
journal={Natural Language Processing},
year={2024},
pages={1–22},
url={https://www.cambridge.org/core/journals/natural-language-processing/article/contextaware-and-expert-data-resources-for-brazilian-portuguese-hate-speech-detection/7D9019ED5471CD16E320EBED06A6E923#},
}
@inproceedings{salles-etal-2025-hatebrxplain,
title = "{H}ate{BRX}plain: A Benchmark Dataset with Human-Annotated Rationales for Explainable Hate Speech Detection in {B}razilian {P}ortuguese",
author = "Salles, Isadora and
Vargas, Francielle and
Benevenuto, Fabr{\'i}cio",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.446/",
pages = "6659--6669",
}
FUNDING
