CMFNThinker: A Novel Cross-source Multi-modal Fake News Detection Model
Kaijia Tian, Guozheng Rao, Xin Wang, Mufan Yu, Jiayin Zhang, Li Zhang
Abstract
The rapid development of social media platforms has accelerated the generation and spread of fake news. News on different platforms varies significantly in content and audience. It makes most existing fake news detection models, which rely on single-source datasets, struggle to perform well on news from other sources. Many social platforms also lack high-quality annotated data. To address this problem, we propose a novel cross-source multi-modal fake news detection model named CMFNThinker. CMFNThinker simulates human thinking patterns. It detects fake news across platforms in three stages: summarizing the news content, retrieving similar news posts and reasoning the truthfulness of the news. We conducted extensive experiments on multi-source datasets. The results show that our model outperforms state-of-the-art baseline models by at least 11.3% in macro F1 for cross-source fake news detection.
BibTeX
@inproceedings{icassp2025_cmfnthinkeranove,
title = {CMFNThinker: A Novel Cross-source Multi-modal Fake News Detection Model},
author = {Kaijia Tian and Guozheng Rao and Xin Wang and Mufan Yu and Jiayin Zhang and Li Zhang},
booktitle = {ICASSP 2025},
year = {2025}
}