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@@ -12,8 +12,6 @@ tags:
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  - naturallanguageprocessing
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  - machinelearning
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  pretty_name: HateBR
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- size_categories:
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- - 1M<n<10M
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  ---
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  # HateBR: The Evaluation Benchmark for Brazilian Portuguese Hate Speech Detection
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  Binary classification (offensive vs. non-offensive comments),
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  Offensiveness level (highly, moderately, and slightly offensive messages),
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  Hate speech targets.
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- Each comment was annotated by three expert annotators, resulting in a high level of inter-annotator agreement.
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-
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- We hope that this expert-annotated dataset will contribute to advancing research in hate speech detection within the field of Natural Language Processing.
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  ### Dataset Description
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@@ -40,9 +36,7 @@ We hope that this expert-annotated dataset will contribute to advancing research
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  - **Repository:** https://github.com/franciellevargas/HateBR
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  - **Demo:** NoHateBrazil (Brasil-Sem-Ódio): http://143.107.183.175:14581/
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- - **Paper:**
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-
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- @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", }
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  @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},
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  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#}, }
 
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  - naturallanguageprocessing
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  - machinelearning
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  pretty_name: HateBR
 
 
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  ---
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  # HateBR: The Evaluation Benchmark for Brazilian Portuguese Hate Speech Detection
 
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  Binary classification (offensive vs. non-offensive comments),
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  Offensiveness level (highly, moderately, and slightly offensive messages),
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  Hate speech targets.
 
 
 
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+ Each comment was annotated by 3 (three) expert annotators, resulting in a high level of inter-annotator agreement.
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  ### Dataset Description
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  - **Repository:** https://github.com/franciellevargas/HateBR
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  - **Demo:** NoHateBrazil (Brasil-Sem-Ódio): http://143.107.183.175:14581/
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+ - **Papers:** @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", }
 
 
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  @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},
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  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#}, }