# Interaction-Based Behavioral Analysis of Twitter Social Network Accounts
## 4 Experimental Results  
In this study, behavioral analyses of social media users were performed, and they were classified according to the characteristics and interactions of their accounts. An up-to-date data set consisting of 4209 training and test users was created. Ten different metrics were used in the data set, which could effectively scan the qualities and interactions of each user in their profiles. Data were collected for sixteen periods, or twice a month for eight months. In the data set, simple and cumulative average methods were used with time series for dimension reduction. The variance between the data was corrected using the min-max normalization method.  
A confusion matrix was used for the performance measurement of SVM, K-NN, and ANN classification algorithms. Figure 2 shows the confusion matrix results obtained for each algorithm.  
Examining the confusion matrices, the detection accuracy of the algorithms in the classification is close to each other and their performance is quite high. Success rates, accuracy, precision, recall, and F1-Score values for each classification algorithm were calculated and are presented in Table 6.  
According to the classification results, the average accuracy value is 94.17% for the SVM algorithm, 96.81% for the K-NN algorithm, and 92.23% for the ANN algorithm. In the K-NN algorithm, showing  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{7}{c}{**Support Vector Machine Algorithm**} \\ \hline
**Category** & **n(Truth)** & **N(Classified)** & **Precision** & **Recall** & **F1-Score** & **Accuracy** & **Overall Accuracy** \\ \hline Popular-Active & 1852 & 1791 & 0.96 & 0.93 & 0.95 & 95.04\% \\ \hline Observer-Passive & 887 & 891 & 0.94 & 0.95 & 0.95 & 97.61\% \\ \hline Spam-Bot-Malicious & 1275 & 1332 & 0.91 & 0.95 & 0.93 & 95.69\% \\ \hline \multicolumn{7}{c}{**K-Nearest Neighbors Algorithm**} \\ \hline
**Category** & **n(Truth)** & **N(Classified)** & **Precision** & **Recall** & **F1-Score** & **Accuracy** & **Overall Accuracy** \\ \hline Popular-Active & 1812 & 1791 & 0.97 & 0.96 & 0.97 & 97.09\% \\ \hline Observer-Passive & 840 & 891 & 0.92 & 0.98 & 0.95 & 97.88\% \\ \hline Spam-Bot-Malicious & 1362 & 1332 & 0.99 & 0.97 & 0.98 & 98.65\% \\ \hline \multicolumn{7}{c}{**Artificial Neural Network Algorithm**} \\ \hline
**Category** & **n(Truth)** & **N(Classified)** & **Precision** & **Recall** & **F1-Score** & **Accuracy** & **Overall Accuracy** \\ \hline Popular-Active & 1898 & 1791 & 0.96 & 0.91 & 0.93 & 93.75\% \\ \hline Observer-Passive & 844 & 891 & 0.88 & 0.92 & 0.90 & 95.64\% \\ \hline Spam-Bot-Malicious & 1272 & 1332 & 0.91 & 0.95 & 0.93 & 95.27\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy, precision, recall, F1-Score values for the classification algorithms.  
Figure 2: Confusion matrix results.  
the highest overall accuracy value, the accuracy value is 97.09% for the popular-active class, 97.88% for the observer-passive class, and 98.65% for the spam-bot-malicious class. Examining the F1-Score parameter, which is the harmonic average of precision and sensitivity, and which indicates the overall accuracy of the classification, the highest accuracy was obtained by the K-NN algorithm. The results obtained with K-NN for all classes are higher than the F1-Score values obtained for the other algorithms.  
ROC curves, another indicator of performance measurement in classification, were obtained for each algorithm. Examining the ROC curves, the AUC value of the K-NN algorithm is close to 1. The ROC curve shows that the success of the K-NN algorithm in predicting the class of profiles is clearly distinguishable from other algorithms. SVM and ANN algorithms had AUC values of 0.99 and 0.98, respectively, while the highest value (1.0) was obtained with the K-NN algorithm.  
The results obtained in Figure 3 show that more successful and acceptable results were obtained with the K-NN algorithm.