Learning Complex Heterogeneous Multimodal Fake News via Social Latent Network Inference
Mingxin Li, Yuchen Zhang, Haowei Xu, Xianghua Li, Chao Gao, Zhen Wang
Abstract
With the diversification of online social platforms, news dissemination has become increasingly complex, heterogeneous, and multimodal, making the fake news detection task more challenging and crucial. Previous works mainly focus on obtaining social relationships of news via retweets, limiting the accurate detection when real cascades are inaccessible. Given the proven assessment of the spreading influence of events, this paper proposes a method called HML (Complex Heterogeneous Multimodal Fake News Detection method via Latent Network Inference). Specifically, an improved social latent network inference strategy is designed to estimate the maximum likelihood of news influences under the same event. Meanwhile, a novel heterogeneous graph is built based on social attributes for multimodal news under different events. Further, to better aggregate the relationships among heterogeneous multimodal features, this paper proposes a self-supervised-based multimodal content learning strategy, to enhance, align, fuse and compare heterogeneous modal contents. Based above, a personalized heterogeneous graph representation learning is designed to classify fake news. Extensive experiments demonstrate that the proposed method outperforms the SOTA in real social media news datasets.
BibTeX
@article{Li_Zhang_Xu_Li_Gao_Wang_2025, title={Learning Complex Heterogeneous Multimodal Fake News via Social Latent Network Inference}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/32022}, DOI={10.1609/aaai.v39i1.32022}, abstractNote={With the diversification of online social platforms, news dissemination has become increasingly complex, heterogeneous, and multimodal, making the fake news detection task more challenging and crucial.
Previous works mainly focus on obtaining social relationships of news via retweets, limiting the accurate detection when real cascades are inaccessible. Given the proven assessment of the spreading influence of events, this paper proposes a method called HML (Complex Heterogeneous Multimodal Fake News Detection method via Latent Network Inference). Specifically, an improved social latent network inference strategy is designed to estimate the maximum likelihood of news influences under the same event. Meanwhile, a novel heterogeneous graph is built based on social attributes for multimodal news under different events. Further, to better aggregate the relationships among heterogeneous multimodal features, this paper proposes a self-supervised-based multimodal content learning strategy, to enhance, align, fuse and compare heterogeneous modal contents. Based above, a personalized heterogeneous graph representation learning is designed to classify fake news. Extensive experiments demonstrate that the proposed method outperforms the SOTA in real social media news datasets.}, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Li, Mingxin and Zhang, Yuchen and Xu, Haowei and Li, Xianghua and Gao, Chao and Wang, Zhen}, year={2025}, month={Apr.}, pages={433-441} }