# A Method for Assessing the Performance of e-Government Twitter Accounts
## 4 Methodology
They are indirect measures of the community and information diffusion and discussion commented by the tweets of the involved ministries. * the m/r network degree skewness of the accounts (ego), and
* the assortativity  
are calculated to serve as indicators of small-worlds formation and homophily. Community formation, discussion, and diffusion of information are desirable properties of citizens' engagement. The paper regards two basic axes of Twitter performance:  
**Activity and popularity of the accounts. ** This is measured through a series of indexes. Followers, number of tweets, tweets per day, followers per day, and famous words total effective reach, are used in this analysis. Followers and tweets, plus followers per day and tweets per day, are measures of activity and popularity, and they also show the rate of the network expansion. The fifth index that is added in the analysis is the famous words total effective reach. This counts the potential reach to users, and it is measured summing the followers that the fourteen most popular tweets of the accounts reach. Twrtland.com provided the scores for each one of the 14 famous tweets in order to calculate the total index. This index takes account of the retweets to calculate how many accounts the tweets reach. Thus, the first two of the three types of influence (followers, retweets) [19], are taken into consideration. It should be noted that more indexes could be included in this first axis, since this application describes a framework that could be modified or expanded to include several indexes that measure activity and popularity. All of the indexes will be summarized later using a Principal Component Analysis into just one principal component. The idea is to include, and then summarize, all the decided indexes in just one factor which will, hereafter, describe activity and popularity. Numbers of followers, followers per day, and total effective reach, were adjusted by dividing them by the population of each country before entering the analysis, for comparison. **Community building. ** The second main concern of the paper is to include in the analysis the property of community formation among citizens since community formation, discussion and diffusion of information signify a maturity stage of e-government adoption. The mentions/replies (m/r) networks are a suitable context to search for communities of followers who actively read, retransmit, and comment. Two sub-axes can now be proposed: the relative placement of the ministry accounts within the networks, and the potentiality of the networks to form small-worlds or exhibit homophily. Including the mention/replies network in the analysis, the third type of influence (mentions/replies) are taken into consideration [19]. Thus, the two sub-axes are:  
* Centrality of the ministry accounts within the m/r networks. It is crucial to measure the relative importance of the accounts with regards to the position they have within the m/r networks. That is, how central or how active an e-government account is regarding mentions and replies? Regarding all the mentions and replies within the network of the ministry and its followers, how many of them actually mention and reply to the ministry and not to each other? To measure this, Social Networking Analysis (SNA) centrality indexes are used. Specifically, two measures of centrality are used: the ministry account (ego) normalized betweenness centrality, and the ministry account (ego) normalized in-degree centrality (both calculated using igraph in R). In-degree centrality measures how many mentions/replies the ministry accounts get; it is the total number of followers of the ministries in the m/r networks. Betweenness centrality is equal to the number of shortest paths from all vertices to all others that pass through the ministry node--account. It is considered that a node with high betweenness centrality has a large influence on the transfer of information.