# Fake News Detection on Social Networks: A Survey
## 2 Development and Classification of Fake News Detection Technology  
Many existing studies have adopted an intuitive method: that is, to detect fake news based on the news content. [PERSON] et al. [23] showed that the manual fact-checking method is insufficient when faced with a massive amount of fake news information. They reviewed content-based fake news detection methods adopting machine learning. In addition to content features, many other types of features can be added to the detection model. [PERSON] et al. [14] pointed out that fake news is deliberately written to misguide readers, making it difficult to detect fake news based on its content. It is necessary to include other auxiliary information to assist in making decisions. [PERSON] et al. [13] also indicated that detecting fake news based on shared content is difficult and requires the addition of new information related to the users' profiles. Specifically, multiple features related to each user's Facebook account and some news content features were combined. [PERSON] et al. [24] reported that fake news detection systems are generally divided into news content and social context methods based on their data sources. The social context method focuses on social features such as user interactions and participation based on the given news. In summary, the fake news detection technology has evolved from expert systems to automatic detection, as shown in Figure 2. For the automatic detection technology, the above literature indicates that the news content alone may be not enough and requires additional auxiliary information, such as social context, for fake news detection. There have been various classification methods for detecting fake news in recent years. [PERSON] et al. [15] classified false information and other related terms based on the intention, dividing the existing false information detection methods into content-based, social context-based, feature fusion-based, and deep learning-based methods based on the type of features used. Most of the literature categorizes fake news detection into broader categories, including content-based and contextual aspects [3]. To facilitate the research of fake news detection on social networks, it is necessary to first study the characterizations of fake news and then propose the fake news detection methods. This paper sorts the classification of the detection methods from this perspective, as shown in Figure 3. [PERSON] et al. [14] first used the theory of psychology and sociology to describe the background of fake news detection. Fake news can be categorized into traditional media fake news and social network fake news. The detection of fake news on traditional news media mainly depends on the content of the news, and social contextual information can serve as additional information for detecting fake news on social networks. Then, fake news detection methods can be classified into news content methods and social context methods. Furthermore, the news content model can be divided into knowledge-based and style-based methods. The social context model involves the social participation of relevant users and can be categorized into stance analysis and propagation analysis. Figure 3: Fake news from characterization to detection [12; 14; 17; 25; 26]. Figure 2: Fake news detection has evolved from expert systems to automatic detection. To better understand the fake news detection task, fake news can be investigated from both temporal and spatial dimensions. From the time angle, [PERSON] et al. [17] divided the life cycle of fake news into three stages: creation, publication, and propagation. According to the life cycle of fake news, they detailed the methods for detecting fake news from four aspects: knowledge, writing style, communication mode, and source. From the spatial dimension, [PERSON] et al. [12] divided fake news and its related content into four parts: creator, target victim, content, and social context.