# A Bibliometric Overview of Twitter-Related Studies Indexed in Web of Science
## 4 Results and Discussion
### Science Mapping  
#### 4.2.1 Country Collaboration Network  
Vosviewer presents the country collaboration network based on co-occurrence frequencies. By default, the association strength is employed to normalize the network [45], this method has also been proved as one of the best [36]. The clustering algorithm is based on a weighted and parameterized variant of the well-known modularity function of Newman and Girvan [46].  
Figure 5 shows the top 40 country collaboration network of our retrieved bibliographic data, it is able to reflect the degree of communication between countries as well as the influential countries in this field [47]. Three major communities (with different node colors) can be found from the network. The size of the nodes represents the impact of the country on Twitter-related studies (based on the number of publications). The edges between nodes represent strength of the cooperative relationships between countries.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rank** & **Author Keywords** & **Documents** & **Keyword Plus** & **Documents** \\ \hline
1 & Social media & 4699 & Social media & 1408 \\
2 & Sentiment analysis & 1148 & Media & 776 \\
3 & Social networks & 1015 & Communication & 680 \\
4 & Facebook & 753 & Facebook & 672 \\
5 & Machine learning & 508 & Internet & 613 \\
6 & Big data & 482 & Impact & 540 \\
7 & Social network & 428 & Online & 534 \\
8 & Social network analysis & 390 & News & 444 \\
9 & Internet & 353 & Networks & 412 \\
10 & Text mining & 327 & Model & 405 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Most relevant keywords.  
It can be easily observed that European countries has a highly internal collaboration ties, while for Asian-Pacific countries, North American countries are their most frequent collaboration partners. However, for USA and Canada, they have strong ties with both European and Asian-Pacific countries. There are also close relations between Iberian countries and Latin American counties, naturally, we believe the common language usage among these countries are the main reason of their close ties.  
Table 5 gives the detailed information about the top 10 most productive countries of Twitter-related studies, SCP is the abbreviation of Single Country Publications, and MCP is Multiple Country Publications, MCP Ratio is MCP as a proportion of total publication number. European countries like the UK, Spain, Germany and Italy share a relatively high degree of international collaboration. Despite the fact that China has the highest index, other Asian countries (India and Japan) hold the lowest ratio. From another perspective, English-speaking countries (USA, UK, Australia, Canada) hold a relatively high degree of international collaboration than other countries.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Country** & **Publications** & **SCP** & **MCP** & **MCP Ratio** \\ \hline USA & 5340 & 4626 & 714 & 13.37\% \\ United Kingdom & 1300 & 997 & 303 & 23.31\% \\ China & 1251 & 820 & 431 & 34.45\% \\ Spain & 1098 & 934 & 164 & 14.94\% \\ India & 1086 & 1001 & 85 & 7.83\% \\ Australia & 707 & 523 & 184 & 26.03\% \\ Canada & 620 & 448 & 172 & 27.74\% \\ Japan & 610 & 547 & 63 & 10.33\% \\ Germany & 518 & 372 & 146 & 28.19\% \\ Italy & 510 & 381 & 129 & 25.29\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Top 10 most productive countries.  
Figure 5: Country collaboration network.