# Fake News Detection on Social Networks: A Survey
## 3 Fake News Detection
### Fake News Detection Approaches
#### 3.3.2 Propagation-Based Methods  
Many studies have shown that fake news differs from true news in terms of dissemination methods [48]. A promising fake news detection method involves the study of propagation-based methods. The information propagation on social networks has strong temporal characteristics as follows: sudden updates, fast propagation speed, and rapid disappearance [29]. Based on the factors involved in the news propagation, the propagation-based approaches can be categorized into (1) only considering the propagation pattern of the news itself, known as news cascade, and (2) capturing other additional information, such as comments, during the news propagation process [17]. (1) News cascade  
In recent years, the news cascade, a tree-like structure, has been used to describe news propagation. This structure only represents the dissemination mode of news and does not contain any additional information [17]. Graph neural networks (GNNs) can better simulate the news propagation on social networks, and thus, there are numerous fake news detection methods based on the GNNs. [PERSON] et al. [48] applied the geometric deep learning model to describe the propagation mode of fake news. Four types of features, including user profile, user activity, news cascade, and content, are extracted to describe news, users, and their activity. [PERSON] et al. [49] showed that the news propagation model, including the corresponding tweets and retweets, can be seen as a tree. The tree is composed of multiple cascades, each consisting of a series of tweets/retweets. They proposed a propagation network model called Propagation2 Vec for the early detection of fake news. A hierarchical attention mechanism is adopted to encode the propagation network, which can assign corresponding weights to different cascades. At the same time, a technique for reconstructing a complete propagation network in the early stages has been proposed. [PERSON] et al. [50] used three graph neural network algorithms, including GraphSAGE, GAT and GCN, for fake news detection, with the URL diffusion cascade as the input. An active learning method has also been proposed for sample annotation. Due to the dynamic nature of information diffusion networks in the real world, [PERSON] et al. [51] designed a fake news detection model called the Dynamic Graph Neural Network (DGNF). Specifically, the discrete time dynamic graph (DTDG) is used to model news propagation networks. [PERSON] et al. [52] designed a hypergraph neural network to jointly model multiple propagation trees for fake news detection. [PERSON] et al. [53] used a GNN to model information propagation patterns. Continual learning techniques are used to gradually train GNNs so that they can achieve stable performance across different datasets. [PERSON] et al. [54] proposed the concept of a propagation forest to cluster propagation trees at the semantic level. A new framework based on Unified Propagation Forest (UniPF) was proposed to fully explore the potential correlation between propagation trees and improve the performance of fake news detection. [PERSON] et al. [55] designed a point process model for fake news dissemination on Twitter. In the model, the dissemination of fake news consists of two parts as follows: the cascade of original news and the cascade of assertion of news falsehoods. The experiments indicated that the proposed method is helpful in detecting and mitigating fake news. (2) Propagation graph  
Sometimes, it is also necessary to consider other additional information, such as comments and user characteristics, in the dissemination of information. Various propagation graphs can be created to model the information dissemination process. [PERSON] et al. [17] showed that the propagation-based fake news detection method can be classified into news cascades and self-defined graphs. For self-defined graphs, homogeneous, heterogeneous, or hierarchical networks can be constructed to describe the propagation pattern of fake news. [PERSON] et al.