TY - GEN
T1 - Fact-Augmented Reasoning Model for Fake News Detection
AU - Xiao, Liang
AU - Shi, Chongyang
AU - Hao, Shufeng
AU - Wei, Zeyu
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
PY - 2026
Y1 - 2026
N2 - The spread of fake news poses significant societal risks. While existing detection methods often focus on analyzing news content, they generally lack mechanisms to assess consistency with real-world facts explicitly. Factual verification approaches, on the other hand, rely on static, pre-constructed evidence sets and are limited in their ability to proactively retrieve and evaluate facts. To address these limitations, we propose FAR-FD (Fact-Augmented Reasoning Model for Fake News Detection), a novel approach that leverages large language models (LLMs) and the Retrieval-Augmented Generation (RAG) framework. Specifically, we introduce the Factual Information Retrieval and Evaluation processes to proactively acquire and evaluate external factual information to ensure the validity of factual information. Subsequently, we obtain explainable reasoning-based factual text through the LLM reasoning process, which is fed into an Expert Model for final classification. Extensive experimental results on two public benchmark datasets validate the validity and superior performance of our proposed FAR-FD over state-of-the-art detection models.
AB - The spread of fake news poses significant societal risks. While existing detection methods often focus on analyzing news content, they generally lack mechanisms to assess consistency with real-world facts explicitly. Factual verification approaches, on the other hand, rely on static, pre-constructed evidence sets and are limited in their ability to proactively retrieve and evaluate facts. To address these limitations, we propose FAR-FD (Fact-Augmented Reasoning Model for Fake News Detection), a novel approach that leverages large language models (LLMs) and the Retrieval-Augmented Generation (RAG) framework. Specifically, we introduce the Factual Information Retrieval and Evaluation processes to proactively acquire and evaluate external factual information to ensure the validity of factual information. Subsequently, we obtain explainable reasoning-based factual text through the LLM reasoning process, which is fed into an Expert Model for final classification. Extensive experimental results on two public benchmark datasets validate the validity and superior performance of our proposed FAR-FD over state-of-the-art detection models.
KW - Fake News Detection
KW - Retrieval Augmented Generation
UR - https://www.scopus.com/pages/publications/105022976822
U2 - 10.1007/978-981-95-4109-6_2
DO - 10.1007/978-981-95-4109-6_2
M3 - Conference contribution
AN - SCOPUS:105022976822
SN - 9789819541089
T3 - Communications in Computer and Information Science
SP - 18
EP - 32
BT - Neural Information Processing - 32nd International Conference, ICONIP 2025, Proceedings
A2 - Taniguchi, Tadahiro
A2 - Leung, Chi Sing Andrew
A2 - Kozuno, Tadashi
A2 - Yoshimoto, Junichiro
A2 - Mahmud, Mufti
A2 - Doborjeh, Maryam
A2 - Doya, Kenji
PB - Springer Science and Business Media Deutschland GmbH
T2 - 32nd International Conference on Neural Information Processing, ICONIP 2025
Y2 - 20 November 2025 through 24 November 2025
ER -