## 5 Experiments and Discussion
### Explainable Analysis  
The trust-aware evidence reasoning and evidence aggregation modules made the decision-making process of TRSA more transparent and the results more interpretable. To answer **E3**, we visualized the evidence credibility, attention weight distribution, and implicit biases of evidence when predicting fake news. Figure 6 presents the results for a specific sample in the PHEME testing set.  
* First, we focused on each token in the source information by accumulating the attention values of the interactions between evidence (high-quality comments) and claims (source information) in the information propagation process, which is represented  
Figure 5: Results contrasted among ablation variations on the PHEME and CED datasets. (**a**) Accuracy contrast among ablation variations on the PHEME and CED datasets. (**b**) F1 score contrast among ablation variations on the PHEME and CED datasets.  
Figure 6: A case study on the explainability of TRSA through visualizing the detection process of one example (labeled fake).  
by the size and color of each word. The larger the font, the darker the color of the word, indicating that more attention is assigned to the word in the process of information propagation and the word is more controversial. One can see that _\"Emergency\"_, _\"distress\"_, and _\"# 4U9525\"_ have been widely discussed by users in the process of information propagation, which further demonstrates that our model can automatically capture controversial content;
* Second, we used Gephi to draw the information dispersion network, where the sizes of nodes were determined by their credibility (the higher the credibility of the node, the larger the node). One can see that the black nodes represented source information, and the other nodes represented related forwarding or comment posts. Comments endowed with high credibility weights could be used as evidence to prove that the source information is fake. Consider the following comments. _\"I doubt that any pilot would not say 'Emergency,' but rather 'Mayday'.\" \"No, then you would say 'PANPAN'. Trust me, I'm a pilot! Besides, 'Mayday' is a time when life is in danger.\" \"By the way: Cabin pressure loss in an airliner is a classic case for Mayday! \(\#\) 4u95257\"_. The _\"PANPAN\"_ and _\"Mayday\"_ terms appearing in these comments are internationally used radio crisis call signals, indicating that the \"Emergency\" term in the source information is incorrect. This indicates that the trust-aware evidence reasoning module can provide highly reliable evidence to explain the model results. To measure the support of evidence for results objectively, we examined the implicit bias distribution of evidence by visualizing the aggregation probabilities of the underlying evidence capsules into the high-level category capsule in the evidence aggregation module. One can see that most of the highly credible evidence refutes the source information content;
* To unfold user attention distribution differences between fake and true news content, we randomly selected three fake (0-2) and three true (3-5) news stories, and plotted their token weight distributions based on the attention of the interactions between the evidence and claims. As shown in Figure 7, the horizontal direction from left to right represented the word sequence. In the vertical direction, the first three entries represented fake information (0-2) and the last three represented true information (3-5). One can see that some parts of fake news had attracted widespread attention, while the attention to various components of real news was relatively uniform. The results show that to determine whether a piece of news is fake, one should first examine the distribution of users' attention to news content. The evidence of fake news in terms of users' attention may be unevenly concentrated on certain parts of news content.