2025-01-14-ma25a.md

title	Multi-Task Network Guided Multimodal Fusion for Fake News Detection
booktitle	Proceedings of the 16th Asian Conference on Machine Learning
year	2025
volume	260
series	Proceedings of Machine Learning Research
month	0
publisher	PMLR
pdf	https://raw.githubusercontent.com/mlresearch/v260/main/assets/ma25a/ma25a.pdf
url	https://proceedings.mlr.press/v260/ma25a.html
software	https://github.com/diga7654321/MMFND
openreview	3dzeN4vEFy
abstract	Fake news detection has become a hot research topic in the multimodal domain. Existing multimodal fake news detection research utilizes a series of feature fusion networks to gather useful information from different modalities of news posts. However, how to form effective cross-modal features? And how cross-modal correlations impact decision-making? These remain open questions. This paper introduces MMFND, a multi-task guided multimodal fusion framework for fake news detection , which introduces multi-task modules for feature refinement and fusion. Pairwise CLIP encoders are used to extract modality-aligned deep representations, enabling accurate measurement of cross-modal correlations. Enhancing feature fusion by weighting multimodal features with normalised cross-modal correlations. Extensive experiments on typical fake news datasets demonstrate that MMFND outperforms state-of-the-art approaches.
layout	inproceedings
issn	2640-3498
id	ma25a
tex_title	Multi-Task Network Guided Multimodal Fusion for Fake News Detection
firstpage	813
lastpage	828
page	813-828
order	813
cycles	false
bibtex_editor	Nguyen, Vu and Lin, Hsuan-Tien
editor	given family Vu Nguyen given family Hsuan-Tien Lin
bibtex_author	Ma, Jinke and Zhang, Liyuan and Liu, Yong and Zhang, Wei
author	given family Jinke Ma given family Liyuan Zhang given family Yong Liu given family Wei Zhang
date	2025-01-14
address
container-title	Proceedings of the 16th Asian Conference on Machine Learning
genre	inproceedings
issued	date-parts 2025 1 14
extras