###### Abstract
When aggregating neighborhood information, existing models treat all information equally, which may introduce noise and render semantic representations unreliable [28]. Therefore, it is necessary to mitigate the impact of noise on model detection by calculating the credibility of comments. Second, existing propagation-based research methods mainly model spatial propagating structure characteristics without considering the dynamic evolution of posts over time. As shown in Figure 1, both comments T\({}_{8}\) and T\({}_{9}\) are replying to comment T\({}_{6}\) with the same spatial structure characteristics, but from the perspective of time there are clear differences between them (T\({}_{8}\) is released earlier than T\({}_{9}\), meaning users may already be affected by T\({}_{8}\) when T\({}_{9}\) is released). Recent studies [29; 30; 31] have demonstrated that temporal structure features can capture the dynamic evolution of information in a more fine-grained manner and promote early detection performance. Spatial and temporal structures depict the evolution of news messages from the perspectives of information interaction networks and temporal message propagation, respectively, which are complementary. Therefore, it is necessary to consider both the temporal neighborhood structure characteristics and spatial neighborhood structure characteristics of information. Additionally, existing methods focus on using deep learning models to integrate more external information and automatically mine hidden features to improve fake news detection performance, while with the model complexity increasing, the decision-making process within a model has become more difficult to explain and verify. A psychological research work [32] has shown that the spreading power of false information is closely related to the importance and fuzziness of events. Therefore, it is typically insufficient to simply mark information as false. A model must also automatically provide a judgment basis to enhance interpretability. To alleviate the problems discussed above, we designed a novel model (**Tr**ust-aware evidence **R**easoning and **S**patiotemporal feature **A**ggregation (TRSA)) to discover evidence for interpretable fake news detection. Specifically, we first designed a trust-aware evidence reasoning module to calculate the credibility of posts based on a random walk model to discover high-quality posts as evidence. Then, considering the credibility of evidence, we designed an evidence representation module based on spatiotemporal structure to aggregate the spatiotemporal neighborhood characteristics of message propagation and enhance the reliable representation of evidence. Finally, we detected fake news by aggregating the implicit bias of evidence in source information based on a capsule network. Specifically, we first modeled the semantic interactions between evidence and source information to capture the controversial points (false portions) of source information, and formed an evidence capsule. We then aggregated the implicit bias of each evidence capsule from the source information through a dynamic routing mechanism. This study makes the following contributions and innovations.