Triple Path Enhanced Neural Architecture Search for Multimodal Fake News Detection

TL;DR

This paper introduces MUSE, a neural architecture search model with triple paths designed to improve multimodal fake news detection by enhancing information fusion and generalization across different modalities.

Contribution

The paper presents a novel triple path neural architecture search model that effectively addresses fusion and generalization challenges in multimodal fake news detection.

Findings

MUSE outperforms baseline models in detection accuracy.

MUSE demonstrates stable performance improvements.

The model effectively handles partial-modality fake news.

Abstract

Multimodal fake news detection has become one of the most crucial issues on social media platforms. Although existing methods have achieved advanced performance, two main challenges persist: (1) Under-performed multimodal news information fusion due to model architecture solidification, and (2) weak generalization ability on partial-modality contained fake news. To meet these challenges, we propose a novel and flexible triple path enhanced neural architecture search model MUSE. MUSE includes two dynamic paths for detecting partial-modality contained fake news and a static path for exploiting potential multimodal correlations. Experimental results show that MUSE achieves stable performance improvement over the baselines.