# Exploring and Visualizing Research Progress and Emerging Trends of Event Prediction: A Survey
## 3 Methodology
### Scientometric and Visual Analysis Using CiteSpace  
A number of tools can be used for scientometric and visual analysis of the literature in a research field, including Bibexcel, Gephi, VOSviewer, and CiteSpace. A summary of their advantages and disadvantages is presented in Table 3.  
With the aim of visualizing and analyzing multiple networks to capture hot topics and development trends of event prediction research, we finally chose to use CiteSpace software [9] by importing those literature documents collected from the WoS in a scientometric way. CiteSpace is an academic literature visualization analysis software developed on the basis of data visualization and metrology, which specializes in analyzing potential knowledge in scientific research. Since the software analyzes the structure, rule, and distribution of scientific knowledge presented by means of visualization, the analysis results are called \"scientific knowledge map\" [66]. Specifically, the deduplication function of CiteSpace was first used to remove duplicate documents and retain a single source, after which 4438 valid documents were left. As shown in Figure 2, we counted the number of documents published year by year. It reveals \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tool** & \multicolumn{2}{c}{**Advantages**} & \multicolumn{2}{c}{**Disadvantages**} \\ \hline Bibexcel & \(\bullet\) & Small and practical & \(\bullet\) & Requiring specific input data format \\  & \(\bullet\) & Strong compatibility with other software & \(\bullet\) & Poor visualization ability \\  & \(\bullet\) & & \(\bullet\) & Lack of dynamic analysis \\ \hline \multirow{4}{*}{Gephi} & \(\bullet\) & Large user community & \(\bullet\) & Cumbersome operation of data cleaning and \\  & \(\bullet\) & Supporting multiple data format & data conversion \\  & \(\bullet\) & Strong visualization ability & \(\bullet\) & Limited functions of analyzing scientific literature \\  & \(\bullet\) & & \(\bullet\) & Lack of quantitative indicators for explaining results \\ \hline \multirow{2}{*}{VOSviewer} & \(\bullet\) & User-friendly operation interface & \(\bullet\) & Poor visualization effect of cluster analysis results \\  & \(\bullet\) & Supporting geographic visualization & \(\bullet\) & The node information of the visual network cannot \\  & \(\bullet\) & Supporting multiple data format & & be viewed \\ \hline \multirow{4}{*}{Citespace} & \(\bullet\) & Powerful visualization function based on various criteria & \(\bullet\) & Requiring high computer performance \\  & \(\bullet\) & Capable of analyzing large-scale networks & \(\bullet\) & The old version of the software cannot be used after the \\  & \(\bullet\) & Rich functions for scientific literature analysis & & new version of the software appears \\  & \(\bullet\) & Supporting dynamic analysis & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A summary of scientometric analysis tools. that an overall upward trend occurs, especially since 2020, the number of published papers on event prediction has increased significantly. Among all the documents, 94.9% are articles, 2.7% are review papers, 2.2% are conference papers, and 0.2% are editorial materials. The distribution of the literature types is shown in Figure 3.  
Based on the preprocessed documents, we used CiteSpace software to conduct four types of analyses, including author co-citation network analysis, document co-citation analysis, collaborative institution network analysis, and keyword co-occurrence network analysis. Specifically, the co-citation networks of authors and documents aimed at identifying the influential academic researchers and papers, respectively. The collaborative institution network was composed to identify cooperation patterns among different institutions. Finally, we analyzed the keyword co-occurrence patterns and the timeline of keywords to obtain the development of research hotspots. The key parameters as well as parameter settings involved in analyzing and visualizing the four types of research networks are introduced as follows.