Rethinking Multimodal Fake News Detection in the Generative AI Era

Wenbin Shen; Guoxuan Qin; Guangxu Yao; Baodong Wang; Yuanbo Rui; Zhichao Lian

cs.CLSep 29, 2026

Rethinking Multimodal Fake News Detection in the Generative AI Era

Authors: Wenbin Shen, Guoxuan Qin, Guangxu Yao, Baodong Wang, Yuanbo Rui, Zhichao Lian

Organizations: Nanjing University of Science and Technology, Nanjing, China · University of Chinese Academy of Sciences, Beijing, China

Abstract

Generative content is increasingly entering the production and dissemination of news, transforming fake news from manually fabricated or simply manipulated material into complex forms in which native and generated content jointly participate. Existing multimodal fake news detection research primarily focuses on veracity assessment and rarely characterizes how generativity differences affect the reliability of evidence. In contrast, AIGC detection primarily determines whether content is generated or modified by generative models, but it does not by itself establish whether the underlying news event is true. To bridge the separation between these tasks in data and evaluation, we construct Weibo26, a multimodal fake news detection dataset for generative-content scenarios. On this basis, we propose the Generativity-Aware Hierarchical Reasoning (GAHR) framework, which combines global judgment with local correction so that generativity information participates in news-veracity reasoning. Experiments on multiple existing fake news detection benchmarks and Weibo26 show that GAHR achieves competitive veracity-detection performance while effectively identifying generative content.

Figures & tables

Figure 1: Conceptual organization of multimodal news samples along two distinct dimensions: veracity (real versus fake) and generative involvement (native versus generated). Native samples contain no generated content, whereas generated-content samples contain AI-generated content.

Figure 2: Overview of the six sample types in Weibo26, including their statistics and distribution.

Dataset	Language	Publication year	Time range	Real	Fake	Image
PHEME ( Kochkina et al., 2018 )	English	2017	2014–2015	3,830	1,972	5,802
GossipCop ( Shu et al., 2020 )	English	2020	up to 2020	16,817	5,323	18,417
Fakeddit ( Nakamura et al., 2020 )	English	2020	2008–2019	527,049	628,501	682,996
AMG ( Guo et al., 2025 )	English	2025	2016–2024	3,018	2,004	5,022
Weibo17 ( Jin et al., 2017 )	Chinese	2017	2012–2016	4,779	4,749	9,528
Weibo21 ( Qian et al., 2021 )	Chinese	2021	2014–2021	4,640	4,488	9,128

Table 1: Comparison of Weibo26 with existing multimodal fake news detection datasets.

Figure 3: Overall architecture of the Generativity-Aware Hierarchical Reasoning (GAHR) framework.

Dataset	Method	Year	Accuracy	Fake News			Real News			AUC
Dataset	Method	Year	Accuracy	Precision	Recall	F1-score	Precision	Recall	F1-score	AUC
PHEME	MVAE	2019	0.774 $\pm$ 0.007	0.677 $\pm$ 0.007	0.565 $\pm$ 0.006	0.616 $\pm$ 0.006	0.810 $\pm$ 0.006	0.858 $\pm$ 0.006	0.833 $\pm$ 0.006	0.841 $\pm$ 0.007
	HMCAN	2021	0.840 $\pm$ 0.014	0.724 $\pm$ 0.014	0.736 $\pm$ 0.014	0.730 $\pm$ 0.014	0.889 $\pm$ 0.015	0.883 $\pm$ 0.015	0.886 $\pm$ 0.014	0.907 $\pm$ 0.014
	CAFE	2022	0.816 $\pm$ 0.012	0.724 $\pm$ 0.012	0.606 $\pm$ 0.013	0.659 $\pm$ 0.013	0.847 $\pm$ 0.012	0.904 $\pm$ 0.013	0.873 $\pm$ 0.013	0.884 $\pm$ 0.012
	MRML	2023	0.853 $\pm$ 0.007	0.722 $\pm$ 0.007	0.811 $\pm$ 0.007	0.764 $\pm$ 0.007	0.917 $\pm$ 0.008	0.870 $\pm$ 0.007	0.893 $\pm$ 0.007	0.924 $\pm$ 0.006
	NSLM	2024	0.826 $\pm$ 0.006	0.715 $\pm$ 0.005	0.679 $\pm$ 0.006	0.697 $\pm$ 0.006	0.869 $\pm$ 0.006	0.887 $\pm$ 0.007	0.878 $\pm$ 0.006	0.897 $\pm$ 0.006

Table 2: Veracity detection results on six datasets. The results are reported as mean

\pm

standard deviation over five runs. Red and blue indicate the best and second-best results, respectively.

Dataset	Method	Accuracy	Fake News			Real News			AUC
Dataset	Method	Accuracy	Precision	Recall	F1-score	Precision	Recall	F1-score	AUC
AMG	w/o Text	0.778 $\pm$ 0.010	0.622 $\pm$ 0.010	0.773 $\pm$ 0.010	0.689 $\pm$ 0.009	0.880 $\pm$ 0.010	0.781 $\pm$ 0.009	0.827 $\pm$ 0.009	0.830 $\pm$ 0.010
	w/o Image	0.809 $\pm$ 0.015	0.697 $\pm$ 0.014	0.797 $\pm$ 0.015	0.744 $\pm$ 0.015	0.873 $\pm$ 0.014	0.816 $\pm$ 0.014	0.842 $\pm$ 0.015	0.866 $\pm$ 0.015
	w/o Global	0.817 $\pm$ 0.007	0.708 $\pm$ 0.007	0.757 $\pm$ 0.007	0.732 $\pm$ 0.006	0.872 $\pm$ 0.007	0.845 $\pm$ 0.006	0.858 $\pm$ 0.007	0.894 $\pm$ 0.006
	w/o Local	0.817 $\pm$ 0.014	0.679 $\pm$ 0.014	0.844 $\pm$ 0.014	0.754 $\pm$ 0.014	0.908 $\pm$ 0.014	0.806 $\pm$ 0.013	0.854 $\pm$ 0.014	0.891 $\pm$ 0.013
	Full Model	0.843 $\pm$ 0.007	0.706 $\pm$ 0.007	0.870 $\pm$ 0.006	0.779 $\pm$ 0.006	0.932 $\pm$ 0.007	0.830 $\pm$ 0.007	0.878 $\pm$ 0.007	0.925 $\pm$ 0.006

Table 3: Ablation results of GAHR on AMG, Weibo21, and Weibo26. The results are reported as mean

\pm

standard deviation over five runs. Red and blue indicate the best and second-best results, respectively.

Method	Accuracy	Generated Content			Native Content			AUC
Method	Accuracy	Precision	Recall	F1-score	Precision	Recall	F1-score	AUC
BERT	0.770 $\pm$ 0.013	0.681 $\pm$ 0.012	0.638 $\pm$ 0.013	0.659 $\pm$ 0.012	0.810 $\pm$ 0.013	0.838 $\pm$ 0.013	0.824 $\pm$ 0.013	0.844 $\pm$ 0.013
RoBERTa	0.780 $\pm$ 0.008	0.696 $\pm$ 0.007	0.655 $\pm$ 0.007	0.674 $\pm$ 0.008	0.820 $\pm$ 0.007	0.845 $\pm$ 0.008	0.832 $\pm$ 0.007	0.855 $\pm$ 0.008
MPU	0.800 $\pm$ 0.014	0.727 $\pm$ 0.014	0.678 $\pm$ 0.014	0.702 $\pm$ 0.014	0.832 $\pm$ 0.015	0.863 $\pm$ 0.015	0.847 $\pm$ 0.015	0.877 $\pm$ 0.015
DP-Net	0.810 $\pm$ 0.013	0.746 $\pm$ 0.013	0.688 $\pm$ 0.013	0.716 $\pm$ 0.013	0.838 $\pm$ 0.013	0.873 $\pm$ 0.014	0.855 $\pm$ 0.014	0.890 $\pm$ 0.013
GAHR-T (Ours)	0.821 $\pm$ 0.013	0.766 $\pm$ 0.013	0.704 $\pm$ 0.012	0.733 $\pm$ 0.013	0.848 $\pm$ 0.013	0.883 $\pm$ 0.013	0.865 $\pm$ 0.014	0.908 $\pm$ 0.012

Table 4: Unimodal text-side AIGC detection results on Weibo26. Red and blue indicate the best and second-best results, respectively.

Method	Accuracy	Generated Content			Native Content			AUC
Method	Accuracy	Precision	Recall	F1-score	Precision	Recall	F1-score	AUC
LGrad	0.822 $\pm$ 0.006	0.676 $\pm$ 0.006	0.759 $\pm$ 0.006	0.715 $\pm$ 0.006	0.892 $\pm$ 0.006	0.845 $\pm$ 0.006	0.867 $\pm$ 0.006	0.866 $\pm$ 0.005
FreqNet	0.833 $\pm$ 0.011	0.692 $\pm$ 0.012	0.773 $\pm$ 0.011	0.730 $\pm$ 0.011	0.897 $\pm$ 0.011	0.851 $\pm$ 0.012	0.875 $\pm$ 0.011	0.876 $\pm$ 0.011
SAFE	0.882 $\pm$ 0.007	0.776 $\pm$ 0.007	0.839 $\pm$ 0.007	0.806 $\pm$ 0.006	0.928 $\pm$ 0.006	0.896 $\pm$ 0.006	0.912 $\pm$ 0.007	0.924 $\pm$ 0.006
UnivFD	0.935 $\pm$ 0.015	0.860 $\pm$ 0.014	0.908 $\pm$ 0.015	0.884 $\pm$ 0.015	0.958 $\pm$ 0.014	0.937 $\pm$ 0.016	0.947 $\pm$ 0.014	0.966 $\pm$ 0.015
DFFreq	0.922 $\pm$ 0.010	0.841 $\pm$ 0.010	0.899 $\pm$ 0.010	0.869 $\pm$ 0.010	0.955 $\pm$ 0.010	0.927 $\pm$ 0.010	0.941 $\pm$ 0.010	0.957 $\pm$ 0.010

Table 5: Unimodal image-side AIGC detection results on Weibo26. Red and blue indicate the best and second-best results, respectively.

Figure 4: Veracity detection with and without generativity labels across the six Weibo26 sample types.

Figure 5: t-SNE visualization of generativity-related representations before and after GAHR processing.

Figure 6: Effects of the attention temperature

\tau

and the generativity-detection loss weight

\lambda_{G}

on model performance.

Figure 7: Representative cases under different types of generative involvement.

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Abstract

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