###### Abstract  
Fake news detection has become a significant topic based on the fast-spreading and detrimental effects of such news. Many methods based on deep neural networks learn clues from claim content and message propagation structure or temporal information, which have been widely recognized. However, firstly, such models ignore the fact that information quality is uneven in propagation, which makes semantic representations unreliable. Additionally, most models do not fully leverage spatial and temporal structures in combination. Finally, internal decision-making processes and results are non-transparent and unexplained. In this study, we developed a trust-aware evidence reasoning and spatiotemporal feature aggregation model for more interpretable and accurate 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 evidence. Next, from the perspective of spatiotemporal structure, we designed an evidence-representation module to capture the semantic interactions granularly and enhance the reliable representation of evidence. Finally, a two-layer capsule network was designed to aggregate the implicit bias in evidence while capturing the false portions of source information in a transparent and interpretable manner. Extensive experiments on two benchmark datasets indicate that the proposed model can provide explanations for fake news detection results, and can also achieve better performance, boosting the F1-score 3.5% on average. fake news detection; explainable machine learning; spatiotemporal structure; social network +
Footnote †: journal: _Applied_  
modeling the text content of news. For instance, early approaches considered linguistic features [5; 6; 7], topics [8] and emotional features [9; 10] in a manual way, and more recent methods have extracted higher-level and implicit semantic information from news' text content using neural networks [11; 12; 13; 14]. These methods have achieved huge success in regular fake news detection. However, compared to news articles, the information published on social media is short and non-standard in format, and contains less effective information with more noise, leading to the issue of sparse semantics [15]. Therefore, it is difficult for previous models which process long and standard text content of regular news to extract key semantic features for detection from the short news posted on social media, due to the semantic sparsity. To alleviate the semantic sparsity issue, recent studies have tried to introduce additional information sources (e.g., social context information). Social context information (e.g., interactions between users and a news story), providing abundant reference information, boasts great potential in alleviating the task, leading to social context-based methods. Concretely, they can be further divided into posts-based and propagation-based methods. Posts-based methods utilize user opinions regarding relevant content to help fake news detection, by modeling the semantic interactions between source information and user comments [16; 17; 18]. Motivated by the posts-based methods, propagation-based methods further consider the local characteristics of semantic interactions in message propagation to capture and distinguish user views regarding source information and comments in a fine-grained manner. Specifically, they model the information propagation process as a graph structure and use graph neural networks to aggregate spatial neighborhood information and learn high-level spatial structure semantic representations [19; 20; 21; 22; 23; 24; 25; 26]. Although the methods discussed above have improved detection performance, they still have some limitations. First, based on the openness and low barrier of entry of social media, there may be artificial accounts [27] attempting to publish incorrect information to affect public opinion during message propagation.