# User Comment-Guided Cross-Modal Attention for Interpretable Multimodal Fake News Detection
## 5 Results and Discussion
### Analysis of UC-CMAF Components  
-CMA (w/o Cross-Modal Attention): This component serves as the core mechanism for connecting information from different modalities, responsible for learning the interactive relationships among text, images, and user comments. The cross-modal attention mechanism dynamically fuses multimodal features through attention weights, enabling the model to capture semantic associations across modalities, identify consistency and contradictions between different information sources, thereby providing crucial judgment basis for fake news detection. -UCI (w/o Comment-Image Fusion): This component specifically handles the fusion process between user comments and image content. Through deep learning techniques, it combines users' textual comments with visual information, capturing users' emotional tendencies and semantic understanding of image content. This fusion mechanism can identify users' judgments about image authenticity in their comments, as well as the degree of matching between images and comment content. -UCT (w/o Comment-Text Fusion): This component is responsible for effectively fusing user comments with original text content. Through contrastive learning or attention mechanisms, it identifies consistency, contradictions, or complementary information between user comments and the main text, mining users' collective judgment on text content credibility and providing verification information from the user perspective to the model. -Comments (w/o User Comments): This component collects and processes feedback information from users, which typically contains rich subjective judgments and emotional expressions. The user comment module can capture collective wisdom in social media environments, identifying users' questioning, support, or neutral attitudes toward content authenticity, providing important social verification signals for the model. -Images (Text + Comments Only): This component is responsible for processing and analyzing visual content, extracting image features and identifying visual clues that may indicate misleading or false information. The image module analyzes technical characteristics, semantic content, and potential tampering traces in images through computer vision techniques, providing visual evidence support for multimodal fake news detection. -Adaptive Weights: This component dynamically adjusts the importance weights of various modalities and components according to the characteristics of different samples,  
Figure 4: Heatmaps of accuracy, precision, recall, and F1 Score for 15 representative models on the ReCOVery (**left**) and MMCoVaR (**right**) datasets. implementing personalized multimodal information fusion strategies. The adaptive weight mechanism can automatically optimize the contribution of different information sources based on input data characteristics, improving the model's adaptability and accuracy when facing different types of fake news. The results of the ablation study (Table 3) indicate that the complete versions of both models exhibit excellent performance, with MMCoVaR (accuracy: 0.931; F1 Score: 0.909) slightly outperforming ReCOVery (accuracy: 0.927; F1 Score: 0.894) across all metrics, demonstrating the superior effectiveness of its architectural design. An analysis of the importance of each component reveals that the user comment module is the most critical in both models, with its removal leading to the largest decline in F1 Score (approximately 0.050 for both ReCOVery and MMCoVaR), indicating that user comments contain vital information that traditional text and image analysis struggle to capture and underscoring the irreplaceable value of subjective judgment and collective intelligence in detecting misinformation.