## 4 Analysis and Findings
### An Association Rules Approach  
This subsection continues the analysis of the previous one. The proposed procedure uses association rules to describe the combinations of accounts followed by the same followers. Association rules are a data mining technique, which are used to manage large data sets and allow the counting of how many  
Figure 2: Distribution of the number of accounts that share followers, using medians. followers follow combinations of accounts and, eventually, arriving at the best rules, such as \"those who follow this set of accounts, also follow this account\". At an elementary stage, the procedure produces results of the form: followers of A are also followers of B, and followers of C, _etc._ For example, the procedure produces combinations of the form, aktofilaki, Helenic.police, pirosvestiki (974), to describe that 974 followers are common to the accounts, Police, Coastguard and Fire Brigade. It produces an extended list of combinations of Twitter accounts, which are followed by the same followers. It is this kind of finding that we are interested in to record and describe all possible combinations of account sharing followers. To proceed with the technique, we have to define two indexes: support and confidence. Support is the minimum percentage of followers that an account or combination of accounts should have in order to be listed. For example, if support equals 0.50, then any combination of accounts should have at least half of the total number of followers of the data set. We set support equal to 0.00019, in order to allow accounts or a combination of accounts to be included, even if they have as many as 20 followers. For two combinations of accounts, confidence is the percentage of followers of the first combination that are also followers of the second. We set this percentage to 10%, so confidence equals 0.10. Therefore, we considered two combinations of accounts to share followers, even if 10% (or more) of the followers of the one are also followers of the other. Of course confidence can vary. We chose such a low value to allow for as many cases to be recorded as possible. In the abovementioned example, aktofilaki, Helenic.police, pirosvestiki (974), support is 974/107,107. This equals almost 1%. Confidence is equal to the proportion of the followers of the two accounts, who are also followers of the third. We proceeded with generating association rules for the 107,107 followers of the 27 Greek e-government Twitter accounts, using WEKA (Waikato Environment for Knowledge Analysis) data mining software developed and distributed by the University of Waikato, New Zealand [59]. We used association rules in this context as a sophisticated, yet descriptive, technique to better present shared followers. The procedure produced combinations of accounts consisting of just one account, two accounts, _etc._, ending up with combinations of thirteen accounts. Figure 3 presents the numbers of combinations with regards to the number of members (Twitter accounts) in each combination. For example, 25 accounts constitute 25 combinations of one member. There exist 203 combinations of two accounts that share followers. These 203 combinations are of the form \"Helenic.police, pirosvestiki\". They are combinations containing two accounts each, which have the properties of support and confidence. That is, their common followers are at least 20%, and at least 10% of the followers of the first account are also followers of the second account. Further, there are 1074 combinations of three accounts that share followers, _etc._ The most populous combination category is that of seven accounts that share followers. There are 16,746 combinations of 13 accounts that have at least 20 followers in common (support). The distribution presented in Figure 3 is nearly symmetric, tending to by normal. It generally describes the procedure and the results, but of course, it does not provide details on what the combinations are. At this stage, the whole picture is captured only by describing the combination distributions.