## 2 Related Work  
With the in-depth development of cutting-edge technologies such as big data and artificial intelligence, many researchers are attempting to apply these technologies to mine various characteristic signals of fake news, such as text, publishing users, participating users, and communication networks. Overall, we can classify these intelligent detection methods into the following categories:  
Content-based approaches. Early methods based on content mainly focused on manually extracting various lexical, grammatical, or topical features, and using traditional machine learning methods to detect fake news. For example, [PERSON] et al. (2012) found that emotion features are valuable for constructing false information classification models (including positive emotion words, negative words, and cognitive behavior words). On this basis, a time series model was designed to capture the key language differences between true and false information. [PERSON] et al. (2010) used different writing styles to identify  
Figure 1: Schematic diagram of the spatiotemporal structure of information dissemination. false statements. [PERSON] et al. [11] introduced a potential Dirichlet distribution topic model for Twitter reliability evaluation. To avoid handcrafted feature engineering and automatically capture the deep hidden semantic features of text, various deep neural network models have been developed. [PERSON] et al. [33] used recurrent neural networks to mine high-level hidden features in information for fake news detection. [PERSON] et al. [34] applied a pre-training model to detect false information, and achieved good results. [PERSON] et al. [13] constructed a heterogeneous graph containing topic, sentence, and entity information to represent a news document, and developed a novel network to distinguish fake news. These methods were significantly effective for distinguishing false news articles. However, news published on social media is short, leading to the issue of sparse semantics. Therefore, the detection performance of these models was significantly reduced. Social context-based approaches. Social media is essentially a heterogeneous graph that includes users, posts, and other entities, as well as forwarding, commenting, and other relationships. Therefore, we can integrate social context information from different perspectives to perform fake news detection tasks. Social context-based approaches can be further grouped into posts- and propagation-based methods. Posts-based methods mainly rely on user reviews on social media, which can provide useful indicators for distinguishing false information. Therefore, user social responses in terms of emotions, opinions, or stances can be extracted through comments to optimize model detection performance. [PERSON] et al. [20,21] used multitask learning and co-attention networks to capture both source information and comments jointly to improve task performance. [PERSON] et al. [35,36] hypothesized that fake news can often attract attention and arouse or activate emotions. Therefore, news comments (i.e., social emotions) from a crowd should not be ignored. The shortcoming of such models is that they overlook the obvious local characteristics of social media information interactions. Propagation-based methods mainly construct isomorphic or heterogeneous information propagation network modeling interactions between posts or users, mining the information propagating structural characteristics for evaluating the authenticity of one claim. [PERSON] et al. [22] proposed a novel attention network that jointly encodes local semantic information and global propagation structure information for false information detection. [PERSON] et al. [23] devised a two-layer graph neural network (BiGCN) model to capture the bidirectional propagating structure of information.