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 amore robust (n x z)-shot problem, where z represents the number of supplementary features. The proposed CMA achieves state-of-the-art (SOTA) results over three benchmark datasets, utilizing a surprisingly simple linear probing method to classify multimodal fake news with 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: https://github.com/zgjiangtoby/FND_fewshot