# A Method for Assessing the Performance of e-Government Twitter Accounts
## 4 Methodology
Their findings indicate that the Greek Parliament Network is a small-world network, rather dissasortative. [PERSON] _et al._[57] investigated the assortativity index to investigate \"happiness\" in Twitter reciprocal reply networks. They supported that \"happiness is assortative\" and can be detected up to three links away. [PERSON] _et al._[58] evaluated the extent to which features present in a Twitter user's immediate neighbors can improve the inference of attributes possessed by the user. Their findings indicate that the attribute of interest has a moderate to high degree of assortativity. However, it is recently known that clustering coefficient is almost invariant and close to zero, and so is the average shortest path [46]. Thus, in the present analysis, only skewness and assortativity of the mentions/replies networks are used to produce the third PC. [PERSON] [59] introduced a method for assessing blogs' influence. The original idea was to use four established indexes of activity, networking, and popularity, and combine them to estimate an overall score of performance. [PERSON] [59; 60] suggested using a Blogosphere Authority Index as a combination of the three best scores of the four indexes: Network Centrality Score (calculated within the networks of blogs), Hyperlink Authority Score as measure of the blog's authority within blogosphere provided by Technorati.com, Site Traffic Score as a measure of the visits to the blog provided by www.sitemeter.com and Alexa.com traffic rankings, and Community Activity Score as a measure of participation calculated by the blog comments. Using the idea originally introduced by [PERSON] [59; 60], we define a new index which is adjusted to fit Twitter and its specifications and peculiarities. We use the three indexes that describe activity/popularity, m/r network centrality (ego), and m/r community formation to include two main aspects of an e-government maturity procedure: reach to the citizens and citizens' participation. For each one of the three sets of initial variables a Principal Component Analysis (PCA) was done. Each one of the three PCA was forced to result to one PC. So, at this stage, three PC were constructed, one from each PCA on each index set, to summarize the variables of the three index sets. The factor scores of the three PC were then ranked in a descending order so that the larger would take the first ranks. The total rank is the sum of the three ranks minus the largest of the three (the worst rank). Next, the rank of the total rank is calculated. This is the overall performance score of the accounts on Twitter, a Twitter Authority Index:  
\[Rankfinal\ =\ Rank1\ +\ Rank2\ +\ Rank3\ -\ WorstRank \tag{1}\]  
A calculation such as this eliminates the problem of the cases with missing values for some of the three indexes; this sometimes happens in small networks and in cases that the accounts have little activity. In these cases the rank of the relative index is assigned the largest value 56. Next, in the calculation of the final rank, the worst value is taken out. The three ranks of the PC join the calculation have equivalent weights, since besides the activity of an account the potential of the account to form communities where discussion and diffusion of information is taking place is significant. In order to calculate the final score some conditions should apply. The three indexes activity/popularity, centrality, and community formation should by inter-correlated, form a unidimensional overall index, and have a high reliability coefficient, otherwise an overall index calculated as the sum of the three indexes might be meaningful. Thus, in the analysis three steps were taken to ensure that the three conditions hold: a correlation matrix of the three ranked indexes was constructed (correlations should be high enough and statistically significant), a PCA should result in the formation of only one PC (for unidimentionality), and Cronbach's alpha was calculated for the three ranks and it should be larger than 0.70 to ensure internal consistency.