@inproceedings{han-etal-2026-covariance,
title = "Covariance Matrix-Driven Image Channel Allocation for Multimodal Fake News Detection",
author = "Han, Zongliang and
Guo, Wenyu and
Jin, Guoqing and
Liu, Yang and
Song, Yan and
Yu, Dong and
Min, Wang",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.56/",
pages = "1101--1115",
ISBN = "979-8-89176-395-1",
abstract = "With the widespread proliferation of the Internet, the spread of fake news has accelerated significantly, evolving from single-text content to multimodal forms that include images and videos. The task of Multimodal Fake News Detection (MFND) takes both text and relevant images as input for fake news identification. However, issues such as image noise and inaccurate focus of visual features often lead to insufficient attention to critical information within images during multimodal fusion. To effectively address these challenges, we propose a covariance matrix-driven image channel allocation method. This method first expands the number of original channel maps, then evaluates the importance of image channels through the covariance matrix and assigns importance scores to the expanded channel maps, thereby redirecting the focus of visual features. Subsequently, we design a multimodal fusion strategy based on a multilayer co-attention mechanism to achieve dynamic fusion across modalities. Finally, a contrastive learning loss is introduced to enhance the alignment between textual and visual modalities. Extensive experiments demonstrate that our method achieves state-of-the-art performance on three public multimodal fake news detection benchmark datasets."
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<abstract>With the widespread proliferation of the Internet, the spread of fake news has accelerated significantly, evolving from single-text content to multimodal forms that include images and videos. The task of Multimodal Fake News Detection (MFND) takes both text and relevant images as input for fake news identification. However, issues such as image noise and inaccurate focus of visual features often lead to insufficient attention to critical information within images during multimodal fusion. To effectively address these challenges, we propose a covariance matrix-driven image channel allocation method. This method first expands the number of original channel maps, then evaluates the importance of image channels through the covariance matrix and assigns importance scores to the expanded channel maps, thereby redirecting the focus of visual features. Subsequently, we design a multimodal fusion strategy based on a multilayer co-attention mechanism to achieve dynamic fusion across modalities. Finally, a contrastive learning loss is introduced to enhance the alignment between textual and visual modalities. Extensive experiments demonstrate that our method achieves state-of-the-art performance on three public multimodal fake news detection benchmark datasets.</abstract>
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%0 Conference Proceedings
%T Covariance Matrix-Driven Image Channel Allocation for Multimodal Fake News Detection
%A Han, Zongliang
%A Guo, Wenyu
%A Jin, Guoqing
%A Liu, Yang
%A Song, Yan
%A Yu, Dong
%A Min, Wang
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F han-etal-2026-covariance
%X With the widespread proliferation of the Internet, the spread of fake news has accelerated significantly, evolving from single-text content to multimodal forms that include images and videos. The task of Multimodal Fake News Detection (MFND) takes both text and relevant images as input for fake news identification. However, issues such as image noise and inaccurate focus of visual features often lead to insufficient attention to critical information within images during multimodal fusion. To effectively address these challenges, we propose a covariance matrix-driven image channel allocation method. This method first expands the number of original channel maps, then evaluates the importance of image channels through the covariance matrix and assigns importance scores to the expanded channel maps, thereby redirecting the focus of visual features. Subsequently, we design a multimodal fusion strategy based on a multilayer co-attention mechanism to achieve dynamic fusion across modalities. Finally, a contrastive learning loss is introduced to enhance the alignment between textual and visual modalities. Extensive experiments demonstrate that our method achieves state-of-the-art performance on three public multimodal fake news detection benchmark datasets.
%U https://aclanthology.org/2026.findings-acl.56/
%P 1101-1115
Markdown (Informal)
[Covariance Matrix-Driven Image Channel Allocation for Multimodal Fake News Detection](https://aclanthology.org/2026.findings-acl.56/) (Han et al., Findings 2026)
ACL
- Zongliang Han, Wenyu Guo, Guoqing Jin, Yang Liu, Yan Song, Dong Yu, and Wang Min. 2026. Covariance Matrix-Driven Image Channel Allocation for Multimodal Fake News Detection. In Findings of the Association for Computational Linguistics: ACL 2026, pages 1101–1115, San Diego, California, United States. Association for Computational Linguistics.