Cross-Modal Augmentation for Few-Shot Multimodal Fake News Detection

• URL: http://arxiv.org/abs/2407.12880v1
• Date: Tue, 16 Jul 2024 09:32:11 GMT
• Title: Cross-Modal Augmentation for Few-Shot Multimodal Fake News Detection
• Authors: Ye Jiang, Taihang Wang, Xiaoman Xu, Yimin Wang, Xingyi Song, Diana Maynard,
• Abstract summary: Few-shot learning is critical for detecting fake news in its early stages. This paper presents a multimodal fake news detection model which augments multimodal features using unimodal features. The proposed CMA achieves SOTA results over three benchmark datasets.
• Score: 0.21990652930491858
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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}

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