MDF: A Dynamic Fusion Model for Multi-modal Fake News Detection
<|reference_start|>MDF: A Dynamic Fusion Model for Multi-modal Fake News Detection: Fake news detection has received increasing attention from researchers in recent years, especially multi-modal fake news detection containing both text and images. However, many previous works have fed two modal features, text and image, into a binary classifier after a simple concatenation or attention mechanism, in which the features contain a large amount of noise inherent in the data,which in turn leads to intra- and inter-modal uncertainty. In addition, although many methods based on simply splicing two modalities have achieved more prominent results, these methods ignore the drawback of holding fixed weights across modalities, which would lead to some features with higher impact factors being ignored. To alleviate the above problems, we propose a new dynamic fusion framework dubbed MDF for fake news detection. As far as we know, it is the first attempt of dynamic fusion framework in the field of fake news detection. Specifically, our model consists of two main components:(1) UEM as an uncertainty modeling module employing a multi-head attention mechanism to model intra-modal uncertainty; and (2) DFN is a dynamic fusion module based on D-S evidence theory for dynamically fusing the weights of two modalities, text and image. In order to present better results for the dynamic fusion framework, we use GAT for inter-modal uncertainty and weight modeling before DFN. Extensive experiments on two benchmark datasets demonstrate the effectiveness and superior performance of the MDF framework. We also conducted a systematic ablation study to gain insight into our motivation and architectural design. We make our model publicly available to:https://github.com/CoisiniStar/MDF<|reference_end|>
@article{lv2024mdf:,
    title={MDF: A Dynamic Fusion Model for Multi-modal Fake News Detection},
    author={Hongzhen Lv, Wenzhong Yang, Fuyuan Wei, Jiaren Peng and Haokun Geng},
    journal={arXiv preprint arXiv:2406.19776},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.19776},
    primaryClass={cs.MM cs.IR}
}