Cross-modal Contrastive Learning for Multimodal Fake News Detection

TL;DR

This paper introduces COOLANT, a cross-modal contrastive learning framework that improves multimodal fake news detection by enhancing image-text alignment and feature aggregation, achieving state-of-the-art results.

Contribution

The paper proposes a novel contrastive learning approach with an auxiliary task and attention-based feature aggregation for more accurate multimodal fake news detection.

Findings

COOLANT outperforms previous methods on Twitter and Weibo datasets.

The auxiliary loss improves cross-modal alignment accuracy.

Attention-guided feature aggregation enhances decision interpretability.

Abstract

Automatic detection of multimodal fake news has gained a widespread attention recently. Many existing approaches seek to fuse unimodal features to produce multimodal news representations. However, the potential of powerful cross-modal contrastive learning methods for fake news detection has not been well exploited. Besides, how to aggregate features from different modalities to boost the performance of the decision-making process is still an open question. To address that, we propose COOLANT, a cross-modal contrastive learning framework for multimodal fake news detection, aiming to achieve more accurate image-text alignment. To further improve the alignment precision, we leverage an auxiliary task to soften the loss term of negative samples during the contrast process. A cross-modal fusion module is developed to learn the cross-modality correlations. An attention mechanism with an attention guidance module is implemented to help effectively and interpretably aggregate the aligned unimodal representations and the cross-modality correlations. Finally, we evaluate the COOLANT and conduct a comparative study on two widely used datasets, Twitter and Weibo. The experimental results demonstrate that our COOLANT outperforms previous approaches by a large margin and achieves new state-of-the-art results on the two datasets.