# Applications of Advanced Analysis Technologies in Precise Governance of Social Media Rumors
## 2 Literature Mining
### Research Categories of Rumors  
The academic research on rumors began with [PERSON]'s research on rumor governance in Massachusetts in 1944 [4]. In recent years, rumors and rumor refutation have gradually become popular research fields. These researches span sociology, psychology, media, and machine learning [5]. A total 2378 documents in the final database are classified into seven categories, as shown in Table 1. Categories of literature are based on sorting out reading literature. We have some initial categories based on previous review papers [6; 7]. Then, in the order of citation numbers, we sort out reading literature based on title, keywords and abstract to classify each literature into a proper category. A new category is determined when literature cannot be classified into existing categories. This process continues until no new category is found. Table 1 sorts out the related literature on rumors and classifies the research into seven categories. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Categories** & \multicolumn{2}{c}{**Research Directions, Technologies,**} \\  & & & **and Representative Literature** & **Research Object** \\ \hline \multirow{4}{*}{1} & & 1.1 & Rumor’s impact prediction [8] \\  & & 1.2 & Rumor influence maximization [9] \\  & & 1.3 & The extent of rumor influence [10] \\  & & 1.4 & Analysis of rumor influence [11] \\ \hline \multirow{4}{*}{2} & & 2.1 & Stochastic rumor spreading model [12] \\  & & 2.2 & The rumors tracking [13] \\  & & 2.3 & D-K Model [14], M-T Model [15] \\  & & 2.4 & Extensions and improvements of classic SIR Model [16; 17] \\  & & 2.5 & Dynamic models, simulation methods [18] \\  & & 2.6 & Propagation model based on bio-mathematics theory [19] \\  & & 2.7 & Research on rumor spreading based on complex network and social network theory [20; 21; 22] \\ \hline \multirow{4}{*}{3} & & 3.1 & Rumor identification based on text and topic [23; 24] \\  & & 3.2 & Rumor identification based on propagation structure [25; 26] \\  & & 3.3 & Rumor detection over varying time windows. [27] \\ \hline \multirow{4}{*}{4} & & 4.1 & Cross-platform verification [28] \\  & & 4.2 & Comparison of different rumor governance strategies \\  & & 29; 30; 31] \\  & & 4.3 & Rumor governance of emergencies/mass incidents [32] \\  & & 4.4 & Coordinated strategies across different areas or disciplines \\  & & & to combat misinformation [33; 34] \\ \hline \multirow{4}{*}{5} & & 5.1 & The influence of self-media on the rumor spreading [35] \\  & & 5.2 & The influence of attitudes, beliefs/consensus, uncertainty, skepticism, criticism, polarization, etc., on the rumor spreading [36; 37; 38] \\  & & 5.3 & The influence of group arguments and opposing views on the rumor spreading [39; 40] \\  & & 5.4 & The influence of rumor audience’s forgetting and hesitation mechanism on the rumor spreading [41; 42; 43] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Research of rumor classification and technologies. Rumor prevention/governance has become a popular rumor research area in recent years. In this area, scholars mainly focus on: (1) case study [29]; (2) from the perspective of psychology, analyzing the effects of different rumor governance methods on eliminating the trust of false rumors and blocking rapid spread [29; 30; 31]; (3) from the perspective of different discipline area putting forward some directional suggestions on the spread of rumors [33; 34]; (4) using the system dynamic model method to study the popularity of rumors, and then proposing governance countermeasures [48]; (5) the social media rumor control models [49], which applies the trust chain/network of social media users to refute false rumors to achieve controlling rumors. Social media rumors are essentially a kind of public opinion [50], so the precise governance of public opinion is directly related to the precise governance of social media rumors.