Each Fake News Is Fake in Its Own Way: An Attribution Multi-Granularity Benchmark for Multimodal Fake News Detection
Abstract
Social platforms, while facilitating access to information, have also become saturated with a plethora of fake news, resulting in negative consequences. Automatic multimodal fake news detection is a worthwhile pursuit. Existing multimodal fake news datasets only provide binary labels of real or fake. However, real news is alike, while each fake news is fake in its own way. These datasets fail to reflect the mixed nature of various types of multimodal fake news. To bridge the gap, we construct an attributing multi-granularity multimodal fake news detection dataset AMG, revealing the inherent fake pattern. Furthermore, we propose a multi-granularity clue alignment model MGCA to achieve multimodal fake news detection and attribution. Experimental results demonstrate that AMG is a challenging dataset, and its attribution setting opens up new avenues for future research.
Cite
Text
Guo et al. "Each Fake News Is Fake in Its Own Way: An Attribution Multi-Granularity Benchmark for Multimodal Fake News Detection." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I1.31999Markdown
[Guo et al. "Each Fake News Is Fake in Its Own Way: An Attribution Multi-Granularity Benchmark for Multimodal Fake News Detection." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/guo2025aaai-each/) doi:10.1609/AAAI.V39I1.31999BibTeX
@inproceedings{guo2025aaai-each,
title = {{Each Fake News Is Fake in Its Own Way: An Attribution Multi-Granularity Benchmark for Multimodal Fake News Detection}},
author = {Guo, Hao and Ma, Zihan and Zeng, Zhi and Luo, Minnan and Zeng, Weixin and Tang, Jiuyang and Zhao, Xiang},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2025},
pages = {228-236},
doi = {10.1609/AAAI.V39I1.31999},
url = {https://mlanthology.org/aaai/2025/guo2025aaai-each/}
}