## 6 Findings
### Analysis of the Social Networking and Activity Indexes of the Greek Political Blogs
With reference to the factor loadings (Table 3), PC1 refers to a blogs' property that could be described as blogs' attempt to construct ego networks, to belong to many 1-cliques, and to have many outgoing links. Loadings for size of ego network, number of 1-cliques a blog belongs to and outgoing links are over 0.8. This could be interpreted as the property of blogs to try to expand their territory by belonging to many groups and linking to many other blogs. PC2 refers to the property of blogs having many incoming links and normalized betweenness. Loadings for incoming links and normalized betweenness are 0.924 and 0.745, respectively. They are central blogs, regarding betweenness, mainly linked by others and not linking to others. Having in mind that by construction these two PCs are uncorrelated, it is reasonable to argue that they represent two distinct connectivity properties of the blogs. Some blogs serve as authority blogs having many incoming links and centrality, while others strive to expand their influence territory by having many outgoing links, and being members of many cliques or forming large ego-networks. \begin{table}
\begin{tabular}{|l|c|c|c|} \hline  & **Incoming** & **Outgoing** & **Normalized** & **Number of 1-cliques** \\  & **links** & **links** & **betweenness** & **a blog belongs to** \\ \hline Outgoing links & 0.301 ** & & & \\ \hline Normalized betweenness & 0.531 ** & 0.750 ** & & \\ \hline Number of 1-cliques a blog & 0.163 & 0.737 ** & 0.413 ** & \\ belongs to & & & & \\ \hline Size of Ego network & 0.192 * & 0.773 ** & 0.497 ** & 0.795 ** \\ \hline \end{tabular}  
* : \(p<0.05\); **: \(p<0.01\).  
\end{table} 
Table 2: Correlation coefficients among the five social network indexes. \begin{table}
\begin{tabular}{|c|c|c|} \hline  & **PC1 (Total variance** & **PC2 (Total variance** \\  & **explained = 53\%)** & **explained = 32\%)** \\ \hline Size of Ego network & 0.917 & 0.135 \\ \hline Number of 1-cliques a blog belongs to & 0.914 & 0.062 \\ \hline Outgoing links & 0.855 & 0.387 \\ \hline Incoming links & 0.013 & 0.924 \\ \hline Normalized betweenness & 0.494 & 0.745 \\ \hline \end{tabular}
\end{table} 
Table 3: Factor loadings of the Principal Components constructed form the five social network indexes (Varimax rotation). The findings are organized hereafter to respond to three related questions:  
1. How are these properties associated with the blogs' activity? 2. Which are the blogs that perform best regarding the two factor scores? 3. What is the connection, if any, between blogs that have the first properly, _i.e._, they try to expand their territory, with the blogs that have the second property, _i.e._, they are central and are recommended? Activity regarding blogging is conceptualized in this paper, with reference to the bloggers' activity and to the users' community activity. They are measured by counting number of posts to represent blog activity and number of comments to represent community activity. Because both posts and comments may be so many, this paper follows the approach of [PERSON] [53] and records them for a week interval. Measuring activity, although interesting, is not an end in itself. Rather, what is of interest in the analysis is to study the correlations between activity and networking practices. Table 4 presents the correlation coefficients between number of posts and number of comments with the two factor scores. Only correlations between the second factor scores and the two activity indexes are both positive and statistically significant. There is, therefore, strong evidence that only blogs, which are central and recommended by others (with regard to incoming links), are also more active and attract more users' activity and reaction. The construction of overall influence scores is practical and meaningful. By construction, factor scores have a mean equal to zero and a standard deviation equal to 1. It is a common procedure to separate cases (_i.e._, the blogs) using their factor scores.