Cross-Modal Augmentation for Few-Shot Multimodal Fake News Detection
<|reference_start|>Cross-Modal Augmentation for Few-Shot Multimodal Fake News Detection: The nascent topic of fake news requires automatic detection methods to quickly learn from limited annotated samples. Therefore, the capacity to rapidly acquire proficiency in a new task with limited guidance, also known as few-shot learning, is critical for detecting fake news in its early stages. Existing approaches either involve fine-tuning pre-trained language models which come with a large number of parameters, or training a complex neural network from scratch with large-scale annotated datasets. This paper presents a multimodal fake news detection model which augments multimodal features using unimodal features. For this purpose, we introduce Cross-Modal Augmentation (CMA), a simple approach for enhancing few-shot multimodal fake news detection by transforming n-shot classification into a more robust (n $\times$ z)-shot problem, where z represents the number of supplementary features. The proposed CMA achieves SOTA results over three benchmark datasets, utilizing a surprisingly simple linear probing method to classify multimodal fake news with only a few training samples. Furthermore, our method is significantly more lightweight than prior approaches, particularly in terms of the number of trainable parameters and epoch times. The code is available here: \url{https://github.com/zgjiangtoby/FND_fewshot}<|reference_end|>
@article{jiang2024cross-modal,
    title={Cross-Modal Augmentation for Few-Shot Multimodal Fake News Detection},
    author={Ye Jiang, Taihang Wang, Xiaoman Xu, Yimin Wang, Xingyi Song, Diana
  Maynard},
    journal={arXiv preprint arXiv:2407.12880},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.12880},
    primaryClass={cs.LG cs.AI cs.CL}
}