Papers by Fabrício Benevenuto
HateBR: A Large Expert Annotated Corpus of Brazilian Instagram Comments for Offensive Language and Hate Speech Detection (2022.lrec-1)
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| Challenge: | In Brazil, hate speech is prohibited, however the regulation is not effective due to the difficulty of identifying, quantifying and classifying this kind of online content. |
| Approach: | They propose to annotate a large corpus of Brazilian Instagram comments manually and to use it to detect hate speech and offensive language. |
| Outcome: | The HateBR corpus was collected from the comment section of Brazilian politicians’ accounts on Instagram and manually annotated by specialists, reaching a high inter-annotator agreement. |
Rhetorical Structure Approach for Online Deception Detection: A Survey (2022.lrec-1)
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| Challenge: | Existing studies on how people use language to inform and misinform are relevant. |
| Approach: | They analyze how discourse structure is applied to fake news detection on the web and social media. |
| Outcome: | The proposed framework is applied to fake news and fake reviews detection on the web and social media. |
HateBRXplain: A Benchmark Dataset with Human-Annotated Rationales for Explainable Hate Speech Detection in Brazilian Portuguese (2025.coling-main)
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| Challenge: | Hate speech detection systems have been developed to inhibit offensive and hateful language from being published or spread on the Web and social media. |
| Approach: | They propose to use a Portuguese dataset to provide rationales for hate speech detection with text span annotations. |
| Outcome: | The proposed models outperform the baselines in Portuguese and showed that they provide plausible explanations when compared to human annotations. |