Abstract
Existing multimodal fake news detection methods often introduce external information to assist detection. However, most of them rely on entity-level retrieval and are therefore prone to introducing event-irrelevant noise. Meanwhile, existing methods mainly focus on improving overall performance and do not account for differences in the degree of harm posed by different instances of fake news. To address these limitations, we design an Event-Level Evidence Retrieval Framework (ELERF) and propose a Relation-Aware Evidence Graph Network (RAEGNet). ELERF retrieves external evidence based on the complete event semantics of a news item. RAEGNet constructs a directed graph that incorporates news-evidence stance relations and evidence-evidence interaction relations, and introduces a conditional-harm branch to jointly model authenticity and potential harm. Experimental results demonstrate that RAEGNet outperforms multiple baseline methods across all evaluated metrics on Weibo-21, Fakeddit, and our self-constructed SSS dataset.
Keywords
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Publication details
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- Open access
- Green open access
Cite this article
APA 7
Shen, W., Qin, G., Yao, G., Wang, B., Rui, Y., Ba, Z., & Lian, Z. (2026). RAEGNet: Relation-Aware Evidence Graph Network for Harm-Aware Multimodal Fake News Detection. https://omanscience.com/en/articles/raegnet-relation-aware-evidence-graph-network-for-harm-aware-multimodal-fake-news-detection
MLA 9
Shen, Wenbin, et al. "RAEGNet: Relation-Aware Evidence Graph Network for Harm-Aware Multimodal Fake News Detection." https://omanscience.com/en/articles/raegnet-relation-aware-evidence-graph-network-for-harm-aware-multimodal-fake-news-detection.
Chicago (author–date)
Shen, Wenbin, Guoxuan Qin, Guangxu Yao, Baodong Wang, Yuanbo Rui, Zhongjie Ba, and Zhichao Lian. 2026. "RAEGNet: Relation-Aware Evidence Graph Network for Harm-Aware Multimodal Fake News Detection." https://omanscience.com/en/articles/raegnet-relation-aware-evidence-graph-network-for-harm-aware-multimodal-fake-news-detection.
Harvard
Shen, W., Qin, G., Yao, G., Wang, B., Rui, Y., Ba, Z. and Lian, Z. (2026) 'RAEGNet: Relation-Aware Evidence Graph Network for Harm-Aware Multimodal Fake News Detection', Available at: https://omanscience.com/en/articles/raegnet-relation-aware-evidence-graph-network-for-harm-aware-multimodal-fake-news-detection.
Vancouver
Shen W, Qin G, Yao G, Wang B, Rui Y, Ba Z, et al. RAEGNet: Relation-Aware Evidence Graph Network for Harm-Aware Multimodal Fake News Detection. https://omanscience.com/en/articles/raegnet-relation-aware-evidence-graph-network-for-harm-aware-multimodal-fake-news-detection
IEEE
W. Shen, G. Qin, G. Yao, B. Wang, Y. Rui, Z. Ba, and Z. Lian, "RAEGNet: Relation-Aware Evidence Graph Network for Harm-Aware Multimodal Fake News Detection," https://omanscience.com/en/articles/raegnet-relation-aware-evidence-graph-network-for-harm-aware-multimodal-fake-news-detection.