# Fake News Detection on Social Networks: A Survey
## 3 Fake News Detection
### Fake News Detection Approaches
#### 3.3.3 Source-Based Methods
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Detection Approach** & **Main Models** & **Datasets** & **Characteristics** \\ \hline \multirow{8}{*}{Content-based} & \multirow{4}{*}{Knowledge-based} & biLSTM, CompEx [32] & Kaggle, CoAID & The authenticity of the \\  & & GNN [53], & LDN, SIN & news is evaluated \\  & & TransE [34] & Kaggle \(\ast\)\$ BBC news, etc. & news is evaluated \\ \cline{2-4}  & \multirow{4}{*}{Style-based} & CNN, RNN [35], & FA-KES, ISOT & \\  & & BOW, SVM, LR, etc. [25] & Politixtet, BuzzFeed & \\  & & Sequential neural network [36] & Buzzfeed, Random political & \\  & & WE, LFS, KNN, Ensemble [37] & WELFake & \\  & & PCA, CNN-LSTM [38] & FNC & \\  & & CNN-RNN [40] & Weibo dataset & \\  & & SENAD, CNN [41] & FakeNewsNet & \\ \cline{2-4}  & \multirow{4}{*}{Multimodal-based} & \multirow{4}{*}{Residual network, CNN [43]} & Tweet, Weibo & \multirow{4}{*}{Obtain better results than} \\  & & LWPC, LDA, LRN, etc. [44] & Kaggle & \\  & & VAE [45] & Twitter, Weibo & \\  & & CNN, BiLSTM, BERT [46] & Fakeddit & \\  & & ERNIE, CNN, FNN [47] & Weibo & \\ \hline \multirow{8}{*}{Propagation-based} & \multirow{4}{*}{News cascade} & Geometric DL [48] & Twitter & \\  & & Propagation2 Vec [49] & Politixtet, GossipCop & \\  & & GraphSAGE, GAT and GCN [50] & Politixtet & \\  & & Weibo, FakeNewsNet, & \multirow{4}{*}{Evaluate news intention;} & \\  & & Hypergraph NN [52] & FakeNewsNet & \\  & & SNU [58] & FakeNewsNet & \\  & & UniPF [54] & FakeNewsNet, Twitter & \\  & & Point process model [55] & Twitter & \\ \hline \multirow{8}{*}{Propagation graph} & Graph attention networks [56] & Twitter15, Twitter16 & \multirow{8}{*}{Evaluate news intention;} & \\  & & Hierarchical propagation network, GNB, DT [57] & FakeNewsNet & more robust; indirectly \\ \cline{1-1}  & & Stance network, RNN [58] & FakeNewsNet & capture the propagation of \\ \cline{1-1}  & & Tri-relationship, FHFN [59] & FakeNewsNet & the news \\ \cline{1-1}  & & FANG [60] & Twitter & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classifications and comparisons of various fake news detection methods. Figure 5 introduces the concepts related to fake news detection, including the following four aspects: fundamental theory, feature type, detection technique, and detection approach. The fundamental theories include the four-factor theory, Undetusch hypothesis, social identity theory, confirmation bias, desirability bias, Naive realism, etc. [17]. Furthermore, the feature type consists of content-based, i.e., \"linguistic-based\" or \"visual-based\". Propagation-based features can be comment-based and network-based. The comment-based method is a direct method for detecting fake news. Furthermore, different types of networks can be constructed, such as stance networks and diffusion networks, in the process of news dissemination. Existing network metrics (e.g., degree coefficient, clustering coefficient, etc.) can be used as network features, or embedding algorithms can be applied to extract network-embedding representations. Furthermore, user-based features can be categorized into individual level and group level. The credibility of users can be obtained through individual-level features such as registration age and the number of followers/followers. Group-level features are used to describe the characteristics of different communities formed by the spreaders of fake news and true news [14]. Moreover, it is worth mentioning that detection technologies include human-based, AI-based and blockchain-based technologies [3]. Human-based technology relies on knowledge for fake news detection through crowdsourcing and fact-checking techniques. AI-based technology applies shallow or deep machine learning approaches to detect fake news. For the blockchain-based technology, fake news can be detected by checking news sources and tracking news. Finally, as for the detection approach, some of the \"content-based\", \"propagation-based\", or \"source-based\" methods are adopted and illustrated in Section 3.3.