# Fake News Detection on Social Networks: A Survey
## 3 Fake News Detection
### Fake News Detection Approaches
#### 3.3.1 The Content-Based Methods  
(1) Knowledge-based  
For this method, a knowledge base or knowledge graph must first be established. Knowledge can be expressed in the form of triples [17]. Then, the extracted knowledge from a piece of news is compared with the facts in the knowledge base to check its authenticity. The above is actually the implementation process of an automatic fact-checking system which can be categorized into fact extraction and fact checking. Some studies use knowledge-based methods to detect fake news. For example, researchers have been exploring how to realize the automation of fact-checking, using technologies based on natural language processing (NLP) and machine learning to automatically predict the authenticity of reports [31]. [PERSON] et al. [32] proposed a knowledge graph-based (KG) framework which includes the news encoder, the entity encoder, and the final classification layer to detect fake news articles. Firstly, the NLP-based technology is used to encode the headline of the news. Then, named entity recognition (NER) is adopted to recognize and extract named entities from the texts, and then named entity disambiguation (NED) is adopted to map the entities to the KG. The ComplEx model is adopted to obtain the representation of the entities. Finally, the embedding of the two parts is combined to detect fake news. [PERSON] et al. [33] designed a new graph neural model, which compares the news with an external knowledge base. Firstly, a directed heterogeneous document graph containing topics and entities for each type of news is constructed. Then, through a carefully designed entity comparison network, different entity representations are compared. Finally, the above comparison features are fed into the fake news classifiers. [PERSON] et al. [34] proposed a fake news detection method based on incomplete and imprecise knowledge graphs using the TransE and B-TransE methods. Firstly, three different knowledge graphs are created to generate background knowledge. Then, the B-TransE method is used to establish the entity and relation embedding and check whether news articles are authentic. The results show that even an incomplete knowledge graph can be used for fake news detection. (2) Style-based  
The style-based approach can evaluate news intent [17]. The pre-trained language model based on neural networks and deep learning has brought about breakthrough development in natural language processing technology. These provide better ideas for fake news detection. [PERSON] et al. [35] proposed a combination of convolutional neural networks and recurrent neural networks for fake news classification. [PERSON] et al. [25] proposed a theory-driven fake news detection method. The news content is analyzed based on vocabulary, syntax, semantics, and discourse. Furthermore, the supervised machine learning method is used to detect fake news. [PERSON] et al. [36] proposed a linguistic model to extract the syntactic, grammatical, sentimental, and readability features of specific news. Moreover, the sequential learning method based on a neural network is adopted to detect fake news. [PERSON] et al. [10] pointed out that the authors of fake news adopt various stylistic tricks, such as stimulating readers' emotions, to promote their creative success. The role of emotional analysis in detecting fake news has been studied. [PERSON] et al. [37] detected fake news based on linguistic features combined with the word-embedding technology. The extracted linguistic features include syntactic and semantic features. [PERSON] et al. [38] designed a fake news stance detection framework based on titles and news text. Firstly, principal component analysis (PCA) and the Chi-square test are used to extract features, and then the extracted features are fed into the CNN-LSTM classifier. With the development of multimedia technology, fake news tries to attract and mislead consumers by using the multimedia content of images or videos, making visual features become an integral part to fake news. [PERSON] et al.