Triple Path Enhanced Neural Architecture Search for Multimodal Fake News Detection

• URL: http://arxiv.org/abs/2501.14455v2
• Date: Thu, 06 Feb 2025 03:29:43 GMT
• Title: Triple Path Enhanced Neural Architecture Search for Multimodal Fake News Detection
• Authors: Bo Xu, Qiujie Xie, Jiahui Zhou, Linlin Zong,
• Abstract summary: We propose a novel and flexible triple path enhanced neural architecture search model MUSE.<n>MUSE includes two dynamic paths for detecting partial-modality contained fake news and a static path for exploiting potential multimodal correlations.<n> Experimental results show that MUSE achieves stable performance improvement over the baselines.
• Score: 5.251333057002555
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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