GAMED: Knowledge Adaptive Multi-Experts Decoupling for Multimodal Fake News Detection
This addresses the problem of detecting fake news in multimodal content for social media and information verification, with incremental improvements in modeling dynamic modality relationships.
The paper tackles multimodal fake news detection by proposing GAMED, a method that uses modal decoupling and multiple expert networks to enhance feature distinctiveness and cross-modal synergies, achieving better performance than state-of-the-art models on the Fakeddit and Yang datasets.
Multimodal fake news detection often involves modelling heterogeneous data sources, such as vision and language. Existing detection methods typically rely on fusion effectiveness and cross-modal consistency to model the content, complicating understanding how each modality affects prediction accuracy. Additionally, these methods are primarily based on static feature modelling, making it difficult to adapt to the dynamic changes and relationships between different data modalities. This paper develops a significantly novel approach, GAMED, for multimodal modelling, which focuses on generating distinctive and discriminative features through modal decoupling to enhance cross-modal synergies, thereby optimizing overall performance in the detection process. GAMED leverages multiple parallel expert networks to refine features and pre-embed semantic knowledge to improve the experts' ability in information selection and viewpoint sharing. Subsequently, the feature distribution of each modality is adaptively adjusted based on the respective experts' opinions. GAMED also introduces a novel classification technique to dynamically manage contributions from different modalities, while improving the explainability of decisions. Experimental results on the Fakeddit and Yang datasets demonstrate that GAMED performs better than recently developed state-of-the-art models. The source code can be accessed at https://github.com/slz0925/GAMED.