## 5 Conclusions  
The paper presented some quantitative features of the e-government Twitter accounts in Greece, regarding their followers and the fashion that they follow Twitter accounts. While it is interesting to record and measure the number of followers and the accounts they follow, it is also interesting to explore what are the combinations of accounts they choose to follow. By locating them, we can understand whether there are certain patterns of accounts followed, according to the citizens' interests. Two techniques are proposed to represent and describe such combinations of accounts that are followed by the same followers. The use of association rules relies solely on counting cases and provides an exhaustive representation and recording of several patterns regarding accounts with shared followers. To produce a brief representation of the accounts in clusters of those which have common followers, we introduce the use of a suitable similarity measure. Based on this measure, a cluster analysis is used to produce the clusters. MDS is used in a preliminary stage and is useful in the case that the data matrix is very big and dimensionality needs to be reduced. Findings provide a snapshot of what accounts citizens choose to follow in order to stay informed on e-government matters through Twitter. Clusters and groupings of accounts reveal that, at large, citizens choose accounts of services that have common origins, orientations or content. The paper answered the main queries posed in the beginning of Section 3:  
1. It provided numbers of followers for each account. Some accounts are very popular regarding the number of followers, while others have only a few followers. Accounts of services regarding public order, the Prime Minister and promotion of Greek tourism are followed by many citizens;
2. While some citizens follow only one account, there are many citizens who follow several accounts. There exist groups of up to thirteen out of twenty seven accounts that share followers. To answer how many followers follow more than one account, the paper presented detailed counts of followers that follow just one or more than one account;
3. Accounts that share followers are presented in the findings of the association rules approach. The distributions of the number of accounts that every account shares followers with is displayed and discussed;
4. Cluster analysis produced groups of accounts that are followed by the same followers;
5. The analysis of the intended purpose and the content of the accounts that are clustered together show that followers choose which accounts to follow by means of the accounts' thematic similarity. Accounts regarding education, public order and digital services are clustered together, since they share common followers. We may conclude that, to a certain extent, people choose to follow accounts of the shame orientation, content or use. Several previous studies, presented in Section 2.2 of this paper, have explored Twitter use in e-government, either at a local or a national level. They mostly use content analysis, social networking analysis and several Twitter metrics to explore what is the current status of Twitter use and how local and national authorities and individuals address citizens. In this paper, we considered the whole set of Greek government Twitter accounts addressing the citizens nationwide. We used a different approach to examine how citizens choose which account to follow, by examining how accounts share followers. A data mining technique was used to present the distribution of accounts that share followers, and a multivariate statistical technique resulted in locating clusters of accounts that share followers. To a certain extent, these accounts are similar regarding their scope, intended purpose and subject or target audience. The analysis followed an approach from the outside in, meaning that we first considered objective features of the accounts, such as the number of followers and, then, tried to examine what the similarities of the formed clusters are.