## 3 Results
### Research on the Frequency of Use of Keywords (RQ-2 and RQ-3)
#### 3.2.2. Keywords with a Moderate Frequency of Occurrence  
A map of keywords with moderate frequency is shown in Figure 6. To create it, we limited the number of occurrences of keywords in the range from 5 to 11 (which is 80% of all detected keywords according to the Pareto distribution). VOSviewer has identified 140 keywords corresponding to the specified threshold value. The 10 most common keywords are electronic voting (433), security (318), cryptography (314), blockchain (281), protocol (193), privacy (149), authentication (139), networks (85), consensus (67), internet (65), and smart contract (58).  
Figure 6 shows that the key terms were distributed across six clusters. The cluster size indicates the frequency of use of a word or term in the text or its importance in the context  
Figure 5: Fragment of the program code.  
Figure 6: A map of keywords with moderate frequency.  
of analysis. The cluster color can also represent additional information, such as clustering or node categorization, and also indicates different clusters or categories of terms. The closest and most common words in each cluster are (cluster 1) security, privacy, and servers; (cluster 2) electronic voting, cryptography, and authentication; (cluster 3) elections, protocols, and calculations; (cluster 4) blockchain, networks, and consensus; (cluster 5) network, smart contracts, anonymity, and optimization; (cluster 6) security analysis, malware, and biometrics. The conclusions indicate that clusters 1, 2, 3, 5, and 6 represent various applications of electronic voting based on cryptographic protocols and blockchain technology.  
Figure 7 shows a fragment of the coincidence matrix of moderately common keywords in the field of electronic voting based on cryptographic protocols and blockchain technology. Rows and columns represent keywords or terms, and cell values indicate how often these keywords occur together in the analyzed texts. Below are the keywords or terms that occur in the analyzed texts with an average frequency. This helps us to understand which terms are related to each other and which concepts are closely interrelated.  
As can be seen, the most common keywords are the following: secret and ballot (9), communication (7), and ballot and attack (2). Therefore, it can be concluded that to search for semantically rich information, it is necessary to use a semantic extension of the query.  
Identifying such data helps researchers understand which topics and concepts are most closely related in the field of electronic voting. This can help identify the main topics and problems in this area, identify the connections between different concepts, and identify the most relevant terms for further study or analysis. It can also help researchers identify potential areas for further research or development.  
Figure 8 shows the distribution of keywords that occur with moderate frequency, depending on the average publication year (APY). The APY indicates the average year of publication of documents containing the specified keywords. A moderate frequency of keyword usage in different years allows us to identify which terms and concepts have  
Figure 7: Matrix of co-occurrence of moderately frequent keywords in the field of electronic voting.  
remained relevant over time and which have appeared or disappeared. For further research, this helps to identify trends and changes in the use of terminology in a particular area of knowledge over time. Currently, special attention is paid to the analysis of such topics in the field of blockchain, smart contracts, consensus (APY 2020), authentication, privacy, networks (APY 2018), electronic voting, calculations, voting schemes (APY 2016), cryptography, protocols, efficiency (APY 2014), protocols, security, elections (APY 2012), computer software, cryptology, and electronic commerce (APY 2010).