@inproceedings{santos-etal-2026-exploring,
title = "Exploring Knowledge Graphs for Automatic Fake News Detection in {P}ortuguese",
author = "Santos, Lucas dos and
Santos, Manoel Rodrigues Euclides and
Souza, Yuri Silva and
Sousa, Jo{\~a}o Pedro Holanda and
Santos, Roney Lira de Sales",
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. 1",
month = apr,
year = "2026",
address = "Salvador, Brazil",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.propor-1.44/",
pages = "446--455",
ISBN = "979-8-89176-387-6",
abstract = "The proliferation of fake news in digital environments poses serious challenges to democratic processes, particularly in morphologically rich languages such as Portuguese. While most existing approaches focus on stylistic cues or propagation patterns in social networks, this paper proposes an automated fake news verification methodology grounded in Knowledge Graphs (KGs). Instead of treating news as raw text, we represent each article as a set of factual events encoded as semantic triples of subject, predicate, and object. A proprietary knowledge graph is built from Brazilian data sources, and a verification algorithm is introduced to estimate the veracity of news articles based on graph connectivity evidence. Experimental results confirm the feasibility of the proposed approach and highlight its inherent explainability as a key advantage over deep learning black-box models. Error analysis further indicates that the main limitation stems from the syntactic complexity of Open Information Extraction in Portuguese, suggesting that improvements at this extraction stage are essential to increase system robustness."
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%0 Conference Proceedings
%T Exploring Knowledge Graphs for Automatic Fake News Detection in Portuguese
%A Santos, Lucas dos
%A Santos, Manoel Rodrigues Euclides
%A Souza, Yuri Silva
%A Sousa, João Pedro Holanda
%A Santos, Roney Lira de Sales
%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. 1
%D 2026
%8 April
%I Association for Computational Linguistics
%C Salvador, Brazil
%@ 979-8-89176-387-6
%F santos-etal-2026-exploring
%X The proliferation of fake news in digital environments poses serious challenges to democratic processes, particularly in morphologically rich languages such as Portuguese. While most existing approaches focus on stylistic cues or propagation patterns in social networks, this paper proposes an automated fake news verification methodology grounded in Knowledge Graphs (KGs). Instead of treating news as raw text, we represent each article as a set of factual events encoded as semantic triples of subject, predicate, and object. A proprietary knowledge graph is built from Brazilian data sources, and a verification algorithm is introduced to estimate the veracity of news articles based on graph connectivity evidence. Experimental results confirm the feasibility of the proposed approach and highlight its inherent explainability as a key advantage over deep learning black-box models. Error analysis further indicates that the main limitation stems from the syntactic complexity of Open Information Extraction in Portuguese, suggesting that improvements at this extraction stage are essential to increase system robustness.
%U https://aclanthology.org/2026.propor-1.44/
%P 446-455
Markdown (Informal)
[Exploring Knowledge Graphs for Automatic Fake News Detection in Portuguese](https://aclanthology.org/2026.propor-1.44/) (Santos et al., PROPOR 2026)
ACL
- Lucas dos Santos, Manoel Rodrigues Euclides Santos, Yuri Silva Souza, João Pedro Holanda Sousa, and Roney Lira de Sales Santos. 2026. Exploring Knowledge Graphs for Automatic Fake News Detection in Portuguese. In Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 1, pages 446–455, Salvador, Brazil. Association for Computational Linguistics.