## 6 Findings
### Analysis of the Social Networking and Activity Indexes of the Greek Political Blogs
Blogs with factor scores greater than 1 are usually considered to be the ones that have the highest values of the factor scores and therefore of the proposed indexes, while those blogs with factor scores smaller than \(-1\) are considered to have the lowest values of the factor scores. This finding also holds for the original variables, which served to form the factor. High factor scores provided that factor loadings are positive, and are associated with high values of the original indexes. For the two PCs constructed by PCA, we can distinguish those blogs having factor scores greater than one for both PCs. Table 5 presents the descriptive statistics for blogs having factor scores over 1 in PC1 and PC2. For convenience these blogs may be considered as best performing according to either PC1 or PC2. Twelve blogs, that is 9.5% of the 127 blogs, belong to the group of blogs having the property of striving to link to many other blogs. Twenty blogs, that is 15.7% of the 127 blogs, receive many incoming links, are more central and present the highest activity, regarding both blog activity and community activity. The 20 blogs having PC2 scores over unity are considered to be authority or central blogs, because they are heavily linked and they present high frequencies of posts and comments (Table 5). \begin{table}
\begin{tabular}{|c|c|c|} \hline  & **Number of posts** & **Number of comments** \\ \hline PC1 & \(-0.019\) & 0.136 \\ \hline PC2 & 0.429 * & 0.408 * \\ \hline \multicolumn{3}{|c|}{*: \(p<0.01\).} \\ \end{tabular}
\end{table} 
Table 4: Correlation coefficients between Principal Components (PC) scores and activity indexes. To answer to the third question, that is, what is the connection between best performing blogs regarding PC1 and best performing blogs regarding PC2, a useful and easy to comprehend approach is to turn to the blogs social network and corresponding adjacency matrix. First blogs in the rows of the matrix are labeled using two labels, blogs with PC1 scores over 1 and blogs with PC1 scores under 1, and similarly, blogs in the columns of the matrix are labeled as blogs with PC2 scores over 1 or blogs with PC2 scores under 1. Therefore, four groups of blogs are created and they can be presented in a kind of contingency-linkage table:  
* blogs with PC1 scores \(>1\) that link to blogs with PC2 scores \(>1\);
* blogs with PC1 scores \(>1\) that link to blogs with PC2 scores \(<1\);
* blogs with PC1 scores \(<1\) that link to blogs with PC2 scores \(>1\);
* blogs with PC1 scores \(<1\) that link to blogs with PC2 scores \(<1\). Then, we can count the number of actual links between blog groups, and calculate a percentage by dividing this number by the total number of links that could exist between blog groups, if all the blogs of group 1 linked to all the blogs of group 2. These percentages serve as measurements of intergroup linkages. Large percentages imply that many of the possible links between groups actually exist. Percentages are presented in Table 6. It becomes clear that 31.67% of the possible links from blogs having PC1 scores over one are actually used to connect with blogs having PC2 scores over one. Similarly, 9.19% of all the possible links from blogs with PC1 scores over one are indeed used to connect with blogs having PC2 scores under one, _etc_. It is quite clear that the biggest percentage of links exist between blogs best performing regarding PC1 and best performing regarding PC2. Blogs that strive to expand their network territory heavily link central and active blogs.