Abstract: The current growth of information exhibits an exponential trend, with fake news becoming a focal issue for both the public and governments. Existing fact-checking-based fake news detec-tion methods face two challenges: a heavy reliance on fact-checking reports, a lack of explanatory evidence related to the original reports, and a shallow level of feature interaction. To address these challenges, this study proposes a Reading-aware Fusion Fact Reasoning Network for explainable fake news de-tection. In the aspect of extractive evidence for explainability, a Hierarchical Encoding Layer is constructed to capture sen-tence-level and document-level feature representations, followed by a Fact Reasoning Layer to obtain report and sentence repre-sentations most relevant to the claim, thereby reducing the mod-el's reliance on fact-checking reports. Inspired by reading be-haviors, which often involve repeatedly reading the claim and corresponding report during information verification, the Read-ing-aware Fusion Layer is introduced to learn the deep interde-pendencies among the claim, evidence, and report feature repre-sentations, enhancing semantic integration. Extensive experi-ments were conducted on the publicly available RAWFC and LIAR fake news datasets. The experimental results demonstrate that RFFR outperforms leading advanced baselines on both datasets.

Bofan Wang and Shenwu Zhang, “A Reading-Aware Fusion Fact Reasoning Network for Explainable Fake News Detection” International Journal of Advanced Computer Science and Applications(IJACSA), 16(6), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160625

@article{Wang2025,
title = {A Reading-Aware Fusion Fact Reasoning Network for Explainable Fake News Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160625},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160625},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {Bofan Wang and Shenwu Zhang}
}