@inproceedings{he-etal-2026-sspc,
title = "{SSPC}: Semantic-Structural Propagation Calibration for Multimodal Fake News Detection",
author = "He, Zejiang and
Luo, Yun and
Huang, Zhen and
Tian, Zhiliang and
Huang, Jingyuan and
Liu, Shanshan and
Ma, Yanzhou and
Zhang, Zhaoning and
Li, Dongsheng",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1279/",
pages = "29155--29176",
ISBN = "979-8-89176-459-0",
abstract = "Multimodal fake news detection predicts post veracity from text, images, and inter-post context. Existing content-centric and graph-propagation methods model post evidence and inter-post context, but graph propagation still assumes connected posts provide compatible veracity evidence. In multimodal fake news graphs, edges often represent post relatedness rather than veracity consistency, so propagation can pass misleading evidence across conflicting posts. In this paper, we propose Semantic-Structural Propagation Calibration (SSPC), which uses noisy LLM pseudo-labels to calibrate where and how much propagation should be trusted, rather than using them as direct labels. SSPC refines graph edges from high-confidence pseudo-label agreement, computes a graph-encoded base prediction and an explicitly graph-propagated prediction, and learns node-level propagation reliance with a cross-fitted utility gate. Experiments on Twitter, PHEME, and Weibo show SOTA performance over strong multimodal and graph-propagation fake news detection baselines."
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<abstract>Multimodal fake news detection predicts post veracity from text, images, and inter-post context. Existing content-centric and graph-propagation methods model post evidence and inter-post context, but graph propagation still assumes connected posts provide compatible veracity evidence. In multimodal fake news graphs, edges often represent post relatedness rather than veracity consistency, so propagation can pass misleading evidence across conflicting posts. In this paper, we propose Semantic-Structural Propagation Calibration (SSPC), which uses noisy LLM pseudo-labels to calibrate where and how much propagation should be trusted, rather than using them as direct labels. SSPC refines graph edges from high-confidence pseudo-label agreement, computes a graph-encoded base prediction and an explicitly graph-propagated prediction, and learns node-level propagation reliance with a cross-fitted utility gate. Experiments on Twitter, PHEME, and Weibo show SOTA performance over strong multimodal and graph-propagation fake news detection baselines.</abstract>
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%0 Conference Proceedings
%T SSPC: Semantic-Structural Propagation Calibration for Multimodal Fake News Detection
%A He, Zejiang
%A Luo, Yun
%A Huang, Zhen
%A Tian, Zhiliang
%A Huang, Jingyuan
%A Liu, Shanshan
%A Ma, Yanzhou
%A Zhang, Zhaoning
%A Li, Dongsheng
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F he-etal-2026-sspc
%X Multimodal fake news detection predicts post veracity from text, images, and inter-post context. Existing content-centric and graph-propagation methods model post evidence and inter-post context, but graph propagation still assumes connected posts provide compatible veracity evidence. In multimodal fake news graphs, edges often represent post relatedness rather than veracity consistency, so propagation can pass misleading evidence across conflicting posts. In this paper, we propose Semantic-Structural Propagation Calibration (SSPC), which uses noisy LLM pseudo-labels to calibrate where and how much propagation should be trusted, rather than using them as direct labels. SSPC refines graph edges from high-confidence pseudo-label agreement, computes a graph-encoded base prediction and an explicitly graph-propagated prediction, and learns node-level propagation reliance with a cross-fitted utility gate. Experiments on Twitter, PHEME, and Weibo show SOTA performance over strong multimodal and graph-propagation fake news detection baselines.
%U https://aclanthology.org/2026.emnlp-main.1279/
%P 29155-29176
Markdown (Informal)
[SSPC: Semantic-Structural Propagation Calibration for Multimodal Fake News Detection](https://aclanthology.org/2026.emnlp-main.1279/) (He et al., EMNLP 2026)
ACL
- Zejiang He, Yun Luo, Zhen Huang, Zhiliang Tian, Jingyuan Huang, Shanshan Liu, Yanzhou Ma, Zhaoning Zhang, and Dongsheng Li. 2026. SSPC: Semantic-Structural Propagation Calibration for Multimodal Fake News Detection. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 29155–29176, Budapest, Hungary. Association for Computational Linguistics.