@inproceedings{irnawan-etal-2025-claim,
title = "Claim veracity assessment for explainable fake news detection",
author = "Irnawan, Bassamtiano Renaufalgi and
Xu, Sheng and
Tomuro, Noriko and
Fukumoto, Fumiyo and
Suzuki, Yoshimi",
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.270/",
pages = "4011--4029",
abstract = "With the rapid growth of social network services, misinformation has spread uncontrollably. Most recent approaches to fake news detection use neural network models to predict whether the input text is fake or real. Some of them even provide explanations, in addition to veracity, generated by Large Language Models (LLMs). However, they do not utilize factual evidence, nor do they allude to it or provide evidence/justification, thereby making their predictions less credible. This paper proposes a new fake news detection method that predicts the truth or false-hood of a claim based on relevant factual evidence (if exists) or LLM`s inference mechanisms (such as common-sense reasoning) otherwise. Our method produces the final synthesized prediction, along with well-founded facts or reasoning. Experimental results on several large COVID-19 fake news datasets show that our method achieves state-of-the-art (SOTA) detection and evidence explanation performance. Our source codes are available online."
}
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%0 Conference Proceedings
%T Claim veracity assessment for explainable fake news detection
%A Irnawan, Bassamtiano Renaufalgi
%A Xu, Sheng
%A Tomuro, Noriko
%A Fukumoto, Fumiyo
%A Suzuki, Yoshimi
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F irnawan-etal-2025-claim
%X With the rapid growth of social network services, misinformation has spread uncontrollably. Most recent approaches to fake news detection use neural network models to predict whether the input text is fake or real. Some of them even provide explanations, in addition to veracity, generated by Large Language Models (LLMs). However, they do not utilize factual evidence, nor do they allude to it or provide evidence/justification, thereby making their predictions less credible. This paper proposes a new fake news detection method that predicts the truth or false-hood of a claim based on relevant factual evidence (if exists) or LLM‘s inference mechanisms (such as common-sense reasoning) otherwise. Our method produces the final synthesized prediction, along with well-founded facts or reasoning. Experimental results on several large COVID-19 fake news datasets show that our method achieves state-of-the-art (SOTA) detection and evidence explanation performance. Our source codes are available online.
%U https://aclanthology.org/2025.coling-main.270/
%P 4011-4029
Markdown (Informal)
[Claim veracity assessment for explainable fake news detection](https://aclanthology.org/2025.coling-main.270/) (Irnawan et al., COLING 2025)
ACL
- Bassamtiano Renaufalgi Irnawan, Sheng Xu, Noriko Tomuro, Fumiyo Fukumoto, and Yoshimi Suzuki. 2025. Claim veracity assessment for explainable fake news detection. In Proceedings of the 31st International Conference on Computational Linguistics, pages 4011–4029, Abu Dhabi, UAE. Association for Computational Linguistics.