@inproceedings{vargas-etal-2026-socially,
title = "Socially Responsible and Explainable Automated Fact-Checking and Hate Speech Detection",
author = "Vargas, Francielle and
Benevenuto, Fabr{\'i}cio and
Pardo, Thiago A. S.",
editor = "Souza, Marlo and
de-Dios-Flores, Iria and
Santos, Diana and
Freitas, Larissa and
Souza, Jackson Wilke da Cruz and
Ribeiro, Eug{\'e}nio",
booktitle = "Proceedings of the 17th International Conference on Computational Processing of {P}ortuguese ({PROPOR} 2026) - Vol. 2",
month = apr,
year = "2026",
address = "Salvador, Brazil",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.propor-2.10/",
pages = "35--42",
ISBN = "979-8-89176-387-6",
abstract = "This Ph.D. dissertation advances the state-of-the-art in Natural Language Processing (NLP) for Portuguese by proposing new and innovative data resources and explainable methods for hate speech detection and automated fact-checking. The thesis introduces several benchmark datasets for Brazilian Portuguese, HateBR, HateBRXplain, HateBRMoralXplain, MFTCXplain, MOL, and FactNews, which have been widely adopted by the research community and address critical gaps in the availability of high-quality annotated resources for Portuguese. In addition, this dissertation proposes novel post-hoc and self-explaining NLP methods: Sentence-Level Factual Reasoning (SELFAR), Social Stereotype Analysis (SSA), Contextual Bag-of-Words with Interpretable Input and Feature Optimization (B+M), Supervised Rational Attention (SRA), and Supervised Moral Rational Attention (SMRA). Across multiple tasks and datasets in Portuguese, these methods outperform baselines while improving interpretability and robustness, demonstrating that explainability and performance can be jointly optimized. Finally, this thesis has achieved significant national and international impact, being cited by leading universities and research institutes worldwide and fostering new M.Sc. and Ph.D. research projects in Brazil. Its scientific and social contributions have also been recognized with multiple prestigious national and international awards, including the Google LARA, the Maria Carolina Monard Best Thesis Award in Artificial Intelligence, the Trevisan Prize for Students ``AI for Good'' from Bocconi University for rigorous computer science research in AI with social impact, and the Diversity and Inclusion Award from the Association for Computational Linguistics (ACL). Lastly, this thesis has received two nominations for the Brazilian Computer Society Thesis Awards in Computer Science, and in Multimedia, Hypermedia, and Web."
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<abstract>This Ph.D. dissertation advances the state-of-the-art in Natural Language Processing (NLP) for Portuguese by proposing new and innovative data resources and explainable methods for hate speech detection and automated fact-checking. The thesis introduces several benchmark datasets for Brazilian Portuguese, HateBR, HateBRXplain, HateBRMoralXplain, MFTCXplain, MOL, and FactNews, which have been widely adopted by the research community and address critical gaps in the availability of high-quality annotated resources for Portuguese. In addition, this dissertation proposes novel post-hoc and self-explaining NLP methods: Sentence-Level Factual Reasoning (SELFAR), Social Stereotype Analysis (SSA), Contextual Bag-of-Words with Interpretable Input and Feature Optimization (B+M), Supervised Rational Attention (SRA), and Supervised Moral Rational Attention (SMRA). Across multiple tasks and datasets in Portuguese, these methods outperform baselines while improving interpretability and robustness, demonstrating that explainability and performance can be jointly optimized. Finally, this thesis has achieved significant national and international impact, being cited by leading universities and research institutes worldwide and fostering new M.Sc. and Ph.D. research projects in Brazil. Its scientific and social contributions have also been recognized with multiple prestigious national and international awards, including the Google LARA, the Maria Carolina Monard Best Thesis Award in Artificial Intelligence, the Trevisan Prize for Students “AI for Good” from Bocconi University for rigorous computer science research in AI with social impact, and the Diversity and Inclusion Award from the Association for Computational Linguistics (ACL). Lastly, this thesis has received two nominations for the Brazilian Computer Society Thesis Awards in Computer Science, and in Multimedia, Hypermedia, and Web.</abstract>
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%0 Conference Proceedings
%T Socially Responsible and Explainable Automated Fact-Checking and Hate Speech Detection
%A Vargas, Francielle
%A Benevenuto, Fabrício
%A Pardo, Thiago A. S.
%Y Souza, Marlo
%Y de-Dios-Flores, Iria
%Y Santos, Diana
%Y Freitas, Larissa
%Y Souza, Jackson Wilke da Cruz
%Y Ribeiro, Eugénio
%S Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 2
%D 2026
%8 April
%I Association for Computational Linguistics
%C Salvador, Brazil
%@ 979-8-89176-387-6
%F vargas-etal-2026-socially
%X This Ph.D. dissertation advances the state-of-the-art in Natural Language Processing (NLP) for Portuguese by proposing new and innovative data resources and explainable methods for hate speech detection and automated fact-checking. The thesis introduces several benchmark datasets for Brazilian Portuguese, HateBR, HateBRXplain, HateBRMoralXplain, MFTCXplain, MOL, and FactNews, which have been widely adopted by the research community and address critical gaps in the availability of high-quality annotated resources for Portuguese. In addition, this dissertation proposes novel post-hoc and self-explaining NLP methods: Sentence-Level Factual Reasoning (SELFAR), Social Stereotype Analysis (SSA), Contextual Bag-of-Words with Interpretable Input and Feature Optimization (B+M), Supervised Rational Attention (SRA), and Supervised Moral Rational Attention (SMRA). Across multiple tasks and datasets in Portuguese, these methods outperform baselines while improving interpretability and robustness, demonstrating that explainability and performance can be jointly optimized. Finally, this thesis has achieved significant national and international impact, being cited by leading universities and research institutes worldwide and fostering new M.Sc. and Ph.D. research projects in Brazil. Its scientific and social contributions have also been recognized with multiple prestigious national and international awards, including the Google LARA, the Maria Carolina Monard Best Thesis Award in Artificial Intelligence, the Trevisan Prize for Students “AI for Good” from Bocconi University for rigorous computer science research in AI with social impact, and the Diversity and Inclusion Award from the Association for Computational Linguistics (ACL). Lastly, this thesis has received two nominations for the Brazilian Computer Society Thesis Awards in Computer Science, and in Multimedia, Hypermedia, and Web.
%U https://aclanthology.org/2026.propor-2.10/
%P 35-42
Markdown (Informal)
[Socially Responsible and Explainable Automated Fact-Checking and Hate Speech Detection](https://aclanthology.org/2026.propor-2.10/) (Vargas et al., PROPOR 2026)
ACL