# Interaction-Based Behavioral Analysis of Twitter Social Network Accounts
## 3 Data and Method
### Data Collection and Filtering  
To classify users correctly and make the classification performance high--in addition to parameters used in the literature such as tweets, account age, follower rank, average retweets, and average likes--other parameters such as diameter, density, reciprocity, centralization, and modularity were used. These metrics are the parameters that focus on a different area to reveal many aspects in which social network users interact. The data used to create the data set was collected from Twitter. The metric data forming the data set was extracted using Twitter Rest API V1.1 supporting search/tweet endpoints by means of the SocialBlade and Netlytic platforms. The frequency at which Rest API can extract Twitter data is at 15 min intervals. It allows the return of a maximum of 1000 records each time. The data set, consisting of the parameters mentioned above, had data extracted at two-week intervals for each user over an eight-month period. The data of 4209 users were extracted to be used as training and test data. Of these users, the data of 4014 users were used as training, and the remaining 195 data were used for test data. Among the users in the training set, 1791 people were in the popular-active group, 891 were in the observer-passive group, and 1332 were in the spam-bot-malicious accounts group. Of the test set users, 84 were in the popular-active group, 42 were in the observer-passive group, and 69 were in the spam-bot-malicious group. The data were extracted from Twitter between September 2018 and April 2019. At each periodic scan, a maximum of 1000 of the most recently shared and updated parameters were included in the data set. The total number of tweets with measured interaction was 2,820,030. The data set, which was formed by calculating ten different metrics from 4209 users over a monthly period, consisted of 673,440 metric data. The size of sixteen periods of data was 5120 KB. Table 2 shows the characteristics of the data set. The explanations and purposes of the use of metrics in the data set are given below:  
1. [label=()]
2. Tweets _(TW)_: They are the messages sent by users to communicate with others. These messages are the most effective way to create interaction. The tweet activity of the user presents significant information about his or her character. It is the most effective scale used to detect observer-passive users in particular. 3. Account Age _(AA)_: This refers to time elapsed between the opening of an account and its active time. It is one of the basic criteria for detecting spam-bot-malicious accounts. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Label** & **Quality of Group** & **Training** & **Test** & **Total** \\ \hline
0 & Popular-active users & 1791 & 84 & 1875 \\ \hline
1 & Observer-passive users & 891 & 42 & 933 \\ \hline
2 & Spam-bot-malicious users & 1332 & 69 & 1401 \\ \hline  & Total User Counts in Data Set & & 4209 \\ \hline  & Data Acquisition Process & & 8 months 16 weeks \\ \hline  & Total Number of Metrics & & 673,440 \\ \hline  & Total Data Size & & 5120 KB \\ \hline  & Classification Categories & & 3 \\ \hline  & Tweet Counts with Measured Interaction & & 2,820,030 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Qualities of the data set with label and number of profiles used. * Follower Rank _(FR)_: This is the ratio of the number of following accounts (_FD_) to the number of follower accounts (FW). _FR_ is calculated as shown in Equation (1) below: \[FR=\frac{FD}{FW}.\] (1)  
The popularity of the account increases as this ratio decreases. Popular users have many followers, but they follow few accounts. The opposite is observed in spam-bot-malicious accounts. They have very high number of following accounts, but their follower accounts are very low. * Average Retweet _(AR)_: It refers to a case where a user shares the message of another user in his/her profile. The average retweet is high in popular-active and observer-passive accounts, and it is low in spam-bot-malicious accounts.