# Cross-Modal Fake News Detection Method Based on Multi-Level Fusion Without Evidence
## 6 Conclusions  
This section is not mandatory but can be added to the manuscript if the discussion is unusually long or complex. In today's rapid development of information dissemination, the spread of fake news has become a serious problem worldwide. Against the background of information explosion and multimodal content proliferation in social media, fake news detection faces the dual challenges of cross-modal semantic divide and easy loss of some data after multimodal fusion. In this paper, we propose a cross-modal fake news detection model CM-MLF based on evidence-free multilevel fusion to address the existing challenges. The accuracy and robustness of fake news detection are significantly improved by cross-modal alignment and multilevel feature fusion techniques. Figure 7: The effect of different combinations of weights on the accuracy of the loss function for auxiliary tasks. The main contributions of this paper are as follows:  
1. This paper proposes a cross-modal false news detection model CM-MLF based on evidence-free multilevel fusion. The problem of semantic inconsistency of multimodal features and the problem of information loss are solved by cross-modal alignment and multilevel fusion framework. Experiments on public datasets show that the CM-MLF model outperforms the benchmark algorithm in evaluation metrics such as accuracy, verifying its effectiveness. 2. A two-stage progressive fusion framework is designed to realize a multi-level feature fusion strategy and introduce an ambiguity learning module. The first stage generates primary fusion features through a cross-modal multi-head cross-attention mechanism. The second stage introduces the KL disambiguation score to assist in attention weight allocation, which further improves the model's ability to focus on key features. Fine-grained secondary fusion is realized by adaptively adjusting the secondary fusion weights of text, image and primary fusion features. Experimental results on two public datasets, Weibo and Twitter, show that the CM-MLF model outperforms the existing baseline model in terms of accuracy, precision, recall, and F1 score. The model accuracy reaches 92.2% and 90.1%, respectively, validating its effectiveness and robustness. The ablation experiments further confirmed the necessity of the modules, especially the cross-modal alignment and attention mechanisms that play a key role in performance improvement. Future research efforts will focus on further feature fusion processes. Specifically, it will explore how to incorporate more dimensional features, such as communication features, social features, comment features, and emotion features, into the detection framework to achieve more effective complementation of information across different modalities, so as to further enhance the accuracy and robustness of fake news detection. Conceptualization, P.H. and H.Z. ; methodology, P.H. and H.Z. ; software, H.Z. ; validation, H.Z. and Y.W. ; formal analysis, S.C.; writing--original draft preparation, P.H. and H.Z. ; writing--review and editing, P.H. and H.Z. ; visualization, H.Z. ; supervision, P.H. ; funding acquisition, P.H. and Y.W. All authors have read and agreed to the published version of the manuscript. This research was funded by Hebei based Universities and Shijiazhuang Industry University Research Cooperation Project (2511300301A), Science Research and Development Project of Hebei University of Economics and Business (2022 YB05). The source code and data are publicly available at [[https://www.kaggle.com/datasets/hanxue123/cm-mlf](https://www.kaggle.com/datasets/hanxue123/cm-mlf)]([https://www.kaggle.com/datasets/hanxue123/cm-mlf](https://www.kaggle.com/datasets/hanxue123/cm-mlf)) (accessed on 7 June 2025). The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: