# User Comment-Guided Cross-Modal Attention for Interpretable Multimodal Fake News Detection
## 5 Results and Discussion
### Attention Visualization  
We analyze the semantic alignment between text and images using the cross-modal attention (CMA) module. As shown in Figure 12, the model significantly focuses on the \"data chart\" region of the image when processing text tokens related to semantics such as \"vaccine\", \"efficacy\", and \"95%\". The highest attention weight reaches 0.90, indicating that the model effectively associates quantitative information like vaccine efficacy with the data visualization in the image. Simultaneously, for words like \"clinical\" and \"trial\", the model mainly focuses on the \"lab scene\" region, with attention weights of 0.80 and 0.70, respectively, further validating the model's effectiveness in understanding medical experiment scenarios. Overall, this module demonstrates strong cross-modal semantic alignment capabilities, which help enhance the model's performance in medical text-image tasks.  
Figure 11: Comparison of ReCOVery and MMCoVaR accuracy with memory usage and training time.  
As shown in Figure 13, UC-CMAF assigns high weights of 0.75, 0.80, and 0.70 to Evidence-Doubt, Procedure-Doubt, and Judgment-Support on their logically corresponding dimensions of \"Evidence Sufficiency\", \"Procedure Legitimacy\", and \"Scientific Accuracy\", respectively, while cross-dimension weights remain between 0.05 and 0.15, indicating effective suppression of semantic interference. At the same time, Emotional-Tendency distributes evenly across all four dimensions with weights between 0.20 and 0.30 (with 0.30 on both \"Evidence Sufficiency\" and \"Scientific Accuracy\"), ensuring comprehensive coverage of emotional context. In the stacked bar chart on the right, 95% of the weight for the \"Evidence Sufficiency\" dimension is contributed by Evidence-Doubt (0.75) and Emotional-Tendency (0.30); for \"Procedure Legitimacy\", Procedure-Doubt (0.80) and Emotional-Tendency (0.20) comprise the entire weight; \"Scientific Accuracy\" is dominated by Judgment-Support (0.70) and Emotional-Tendency (0.30); and \"Data Reliability\" is primarily composed of Emotional-Tendency (0.20) and Judgment-Support (0.15), with other categories contributing less than 0.10. This distribution both validates UC-CMAF's high discriminative power in fine-grained intent classification and demonstrates its ability to deeply parse subjective emotional cues.  
As shown in the attention alignment in Figure 14, the UCT module demonstrates highly focused comment-text alignment: Comment 1 places a weight of 0.75 on \"efficacy\" (all other tokens receive weights below 0.10); Comment 2 attends most strongly to \"95%\" with 0.70, and retains minor attention on \"efficacy\" (0.15) and \"phase 3\" (0.05); Comment 3 concentrates on \"phase 3\" with 0.60, while assigning 0.10 each to \"clinical\" and \"efficacy\".  
Figure 12: Cross-modal attention heatmap for text-image semantic alignment.  
Figure 13: Comment type distribution across credibility assessment dimensions.  
Correspondingly, UC-CMAF assigns the highest importance score of 0.92 to Comment 2, with Comment 1 and Comment 3 scoring 0.85 and 0.78, respectively, perfectly mirroring the UCT attention distributions. These results demonstrate that UCT can precisely align user comments with key textual terms, and that UC-CMAF can effectively discriminate comment priority via quantified importance, validating the model's alignment accuracy and discriminative power in multi-comment, multi-text scenarios.