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

TL;DR

This paper introduces Cross-Modal Augmentation (CMA), a lightweight method that significantly improves few-shot multimodal fake news detection by transforming the problem into a more robust classification task, achieving state-of-the-art results.

Contribution

The paper proposes CMA, a novel simple augmentation technique that enhances few-shot multimodal fake news detection and reduces model complexity.

Findings

CMA achieves state-of-the-art results on three benchmark datasets.

The method is more lightweight with fewer trainable parameters.

CMA transforms n-shot into a more robust (n × z)-shot classification.

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}