## 2 Related Work
### False Information Detection Based on Propagation Structure and Content Features  
With the rapid development of graph learning techniques, researchers have discovered significant differences in diffusion topology between false information and authentic content within social networks, prompting propagation path analysis to become one of the core foundations for online rumor identification. The propagation structure feature refers to a pattern feature of false information during its spread, encompassing the interactions among posts, the extent of the formed propagation route, and the duration of the transmission time. Investigators concentrate on discerning the disparities in the propagation traits of deceitful information versus veracious content for detection purposes. Certain academics employ deep learning techniques to extract the sequential attributes of misinformation propagation over time, drawing from the dissemination pathways or networks within social media platforms, intending to formulate a temporal feature model. [PERSON] et al. [28] proposed a Recurrent Neural Network (RNN)-based model that learns deep representations of tweet sequences for false information detection. Some researchers also represent the non-sequential propagation structure features to realize false information detection. [PERSON] et al. [8] use the non-sequential propagation structure of false information to learn the features that distinguish the content of tweets. The complete propagation structure can extract features more comprehensively than the partial structure. [PERSON] et al. [11] used the graph convolutional network method to realize the construction of a false information detection model from two directions of propagation and diffusion. [PERSON] et al. [29] also modeled the false information propagation structure as a bidirectional graph and designed three interpretable bidirectional graph data augmentation strategies. Using node-level and graph-level contrastive learning to capture the propagation characteristics of events. [PERSON] et al. [30] presented a disinformation detection method grounded in a multi-relation propagation tree, addressing the limitation whereby most current propagation characteristic-based approaches merely take into account the overt interaction dynamics during dissemination while neglecting the modeling of latent relationships. [PERSON] et al. [31] introduced a social media rumor detection model integrating dynamic propagation and community structure, which solved the problem of insufficient utilization of time information in existing rumor detection models. Verification using the community structure characteristics of false information propagation can improve the performance of the rumor detection model. [PERSON] et al. [32] represented false information as different propagation threads and designed a hierarchical cue encoding mechanism to learn the context representation of false information from a language-independent perspective. They advocated an innovative zero-shot detection framework predicated on cue learning, which can identify false information from different domains or different languages.  
False information detection methods based on propagation relationships rely on complete network topology data. When there are missing records or incomplete data in the propagation links, their detection accuracy significantly decreases. Furthermore, existing methods mainly focus on the structural features of the propagation path, while neglectingthe semantic information of the propagated content itself. This may lead to misjudgments about the nature of the information, such as its authenticity or emotional tendency.