# Fake News Detection on Social Networks: A Survey
###### Abstract  
In recent years, social networks have developed rapidly and have become the main platform for the release and dissemination of fake news. The research on fake news detection has attracted extensive attention in the field of computer science. Fake news detection technology has made many breakthroughs recently, but many challenges remain. Although there are some review papers on fake news detection, a more detailed picture for carrying out a comprehensive review is presented in this paper. The concepts related to fake news detection, including fundamental theory, feature type, detection technique and detection approach, are introduced. Specifically, through extensive investigation and complex organization, a classification method for fake news detection is proposed. The datasets of fake news detection in different fields are also compared and analyzed. In addition, the tables and pictures summarized here help researchers easily grasp the full picture of fake news detection. fake news; detection; dataset; social networks +
Footnote †: journal: Medical  
news [8]. It may be more accurate to use more objective, comprehensive and verifiable general terms [8; 9]. However, the attention of \"fake news\" has become relatively high recently. The term \"fake news\" was defined by the Macquarie Dictionary in 2021 and by the American Dialect Association and Collins English Dictionary in 2017 [10]. The Google Trends service shows the search popularity of the common terms mentioned above in the past ten years, as shown in Figure 1. Trends shows that the search popularity of these common terms did not significantly differ before June 2016. However, starting from around the second half of 2016, the popularity of the term fake news exceeded that of several other terms. This confirms that the term fake news has been a topic of public debate since the 2016 US presidential election. With the prevalence of COVID-19 and the US presidential election in 2020, the search popularity of the term fake news reached its peak by March 2020. In the past three years, the search index for fake news has generally been higher than other terms. Given the popularity of the term fake news, further analysis needs to be conducted using the term fake news instead of other general terms. For financial and ideological purposes, a significant amount of fake news has been produced and distributed through social networks [11]. [PERSON] et al. [12; 13] showed that fake news has three basic characteristics as follows: volume, velocity, and veracity (3V). This means that fake news is massive, uncertain, and in spread in real time. Most people define fake news as \"deliberate and verifiable false news\" [14; 15]. Authenticity and intention are two important characteristic dimensions of fake news [3; 16]. Table 1 shows the similarities and differences of basic terms related to fake news based on these two important characteristics [3; 17]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Terms** & **Characteristics** & **Authenticity** & **Intent to Harm** \\ \hline \multirow{3}{*}{Fake news} & Commonly used, false and often & \multirow{3}{*}{N} & \multirow{3}{*}{Y} \\  & sensational information & & \\ \cline{1-1} \cline{3-4}  & International organizations like & & \\ \cline{1-1} \cline{3-4} \multirow{2}{*}{Disinformation} & to use, referring to fake and & \multirow{2}{*}{N} & \multirow{2}{*}{Y} \\ \cline{1-1} \cline{3-4}  & harmful information & & \\ \cline{1-1} \cline{3-4} \multirow{2}{*}{Misinformation} & Refers to false content but lacks & \multirow{2}{*}{N} & \multirow{2}{*}{N} \\ \cline{1-1} \cline{3-4}  & potential intent to cause harm & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of terms related to fake news. Figure 1: The Google Trends analysis of common terms related to fake news in the past decade. In order to cope with the chaos of information dissemination in the new media era, some online fact-checking systems have emerged. Fact checking is the task of evaluating whether news content is true.