@inproceedings{zhu-etal-2025-ratsd,
title = "{RATSD}: Retrieval Augmented Truthfulness Stance Detection from Social Media Posts Toward Factual Claims",
author = "Zhu, Zhengyuan and
Zhang, Zeyu and
Zhang, Haiqi and
Li, Chengkai",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.187/",
doi = "10.18653/v1/2025.findings-naacl.187",
pages = "3366--3381",
ISBN = "979-8-89176-195-7",
abstract = "Social media provides a valuable lens for assessing public perceptions and opinions. This paper focuses on the concept of truthfulness stance, which evaluates whether a textual utterance affirms, disputes, or remains neutral or indifferent toward a factual claim. Our systematic analysis fills a gap in the existing literature by offering the first in-depth conceptual framework encompassing various definitions of stance. We introduce RATSD (Retrieval Augmented Truthfulness Stance Detection), a novel method that leverages large language models (LLMs) with retrieval-augmented generation (RAG) to enhance the contextual understanding of tweets in relation to claims. RATSD is evaluated on TSD-CT, our newly developed dataset containing 3,105 claim-tweet pairs, along with existing benchmark datasets. Our experiment results demonstrate that RATSD outperforms state-of-the-art methods, achieving a significant increase in Macro-F1 score on TSD-CT. Our contributions establish a foundation for advancing research in misinformation analysis and provide valuable tools for understanding public perceptions in digital discourse."
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<abstract>Social media provides a valuable lens for assessing public perceptions and opinions. This paper focuses on the concept of truthfulness stance, which evaluates whether a textual utterance affirms, disputes, or remains neutral or indifferent toward a factual claim. Our systematic analysis fills a gap in the existing literature by offering the first in-depth conceptual framework encompassing various definitions of stance. We introduce RATSD (Retrieval Augmented Truthfulness Stance Detection), a novel method that leverages large language models (LLMs) with retrieval-augmented generation (RAG) to enhance the contextual understanding of tweets in relation to claims. RATSD is evaluated on TSD-CT, our newly developed dataset containing 3,105 claim-tweet pairs, along with existing benchmark datasets. Our experiment results demonstrate that RATSD outperforms state-of-the-art methods, achieving a significant increase in Macro-F1 score on TSD-CT. Our contributions establish a foundation for advancing research in misinformation analysis and provide valuable tools for understanding public perceptions in digital discourse.</abstract>
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%0 Conference Proceedings
%T RATSD: Retrieval Augmented Truthfulness Stance Detection from Social Media Posts Toward Factual Claims
%A Zhu, Zhengyuan
%A Zhang, Zeyu
%A Zhang, Haiqi
%A Li, Chengkai
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F zhu-etal-2025-ratsd
%X Social media provides a valuable lens for assessing public perceptions and opinions. This paper focuses on the concept of truthfulness stance, which evaluates whether a textual utterance affirms, disputes, or remains neutral or indifferent toward a factual claim. Our systematic analysis fills a gap in the existing literature by offering the first in-depth conceptual framework encompassing various definitions of stance. We introduce RATSD (Retrieval Augmented Truthfulness Stance Detection), a novel method that leverages large language models (LLMs) with retrieval-augmented generation (RAG) to enhance the contextual understanding of tweets in relation to claims. RATSD is evaluated on TSD-CT, our newly developed dataset containing 3,105 claim-tweet pairs, along with existing benchmark datasets. Our experiment results demonstrate that RATSD outperforms state-of-the-art methods, achieving a significant increase in Macro-F1 score on TSD-CT. Our contributions establish a foundation for advancing research in misinformation analysis and provide valuable tools for understanding public perceptions in digital discourse.
%R 10.18653/v1/2025.findings-naacl.187
%U https://aclanthology.org/2025.findings-naacl.187/
%U https://doi.org/10.18653/v1/2025.findings-naacl.187
%P 3366-3381
Markdown (Informal)
[RATSD: Retrieval Augmented Truthfulness Stance Detection from Social Media Posts Toward Factual Claims](https://aclanthology.org/2025.findings-naacl.187/) (Zhu et al., Findings 2025)
ACL