# Interaction-Based Behavioral Analysis of Twitter Social Network Accounts
## 3 Data and Method
### Methods  
To classify the profiles in the data set into three different categories with respect to their characteristics, support vector machine (SVM), k-nearest neighbor (KNN), and artificial neural network (ANN) algorithms were used. The asset values, effect sizes, qualities, and confidence indices  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Unfiltered Data**} \\ \hline
**User** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** \\ \hline TW & 9192.00 & 30,371.00 & 1408.00 & 10,068.00 & 78.00 \\ \hline AA & 104 & 104 & 128 & 104 & 116 \\ \hline FR & 1 & 2 & 3 & 4 & 5 \\ \hline AR & 1.78 & 69.04 & 6.76 & 7865.42 & 22.54 \\ \hline AL & 12.82 & 198.11 & 360.49 & 43.95 & 83.31 \\ \hline DN & 20.00 & 38.00 & 2.00 & 11.00 & 3.00 \\ \hline CZ & 0.002933 & 0.006929 & 0.003517 & 0.005980 & 0.002025 \\ \hline RC & 0.004575 & 0.000810 & 0.000810 & 0.074400 & 0.029980 \\ \hline DT & 0.290370 & 0.290970 & 0.239170 & 0.096340 & 0.375770 \\ \hline MD & 0.606270 & 0.451470 & 0.627370 & 0.537670 & 0.411870 \\ \hline \multicolumn{5}{c}{**Min-Max Optimization Applied Data**} \\ \hline
**User** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** \\ \hline TW & 0.00000275 & 0.00000275 & 0.00000338 & 0.00000275 & 0.00000307 \\ \hline AA & 0 & 0.006410256 & 0.012820513 & 0.019230769 & 0.025641026 \\ \hline FR & 0.000254714 & 0.009862714 & 0.000965714 & 1.123631429 & 0.003219286 \\ \hline AR & 0.001622405 & 0.025077595 & 0.045632152 & 0.005562911 & 0.01054557 \\ \hline AL & 0.01639443 & 0.0311475 of profiles are determined by the character analysis obtained from the behavioral analysis based on social media interaction. To increase the classification performance, the training set data of algorithms were kept high. Four thousand and fourteen account data were used in the training of the algorithms. Algorithms were implemented on the MATLAB platform, and tested to find out the appropriate parameters for the highest performances with 5-fold cross-validations applied to all classification algorithms. Confusion matrix values of all the applied algorithms were calculated and their receiver operating characteristic (ROC) curves were plotted. A confusion matrix is a popular representation of the performance of classification models. The matrix shows the number of correctly and falsely classified samples compared to the actual results with the test data. One of the advantages of using the confusion matrix as an evaluation tool is that if the data set is imbalanced, it allows a more detailed analysis rather than just a simple proportion of correctly classified samples, which can produce misleading results. The matrix dimensions are \(n\times n\), where \(n\) indicates the number of classes [36]. Table 5 shows the confusion matrix for 2 classes. The performance parameters for the algorithms were calculated by using true positive (_TP_), false positive (_FP_), true negative (_TN_), and false negative (_FN_) values in the confusion matrix. The calculated parameters are Accuracy, Precision, Recall and F1-Score. Accuracy is the ratio of accurate estimates to all estimates in the system:  
\[Accuracy=\frac{(TP+TN)}{(TP+TN+FN+FP)}. \tag{13}\]  
Recall indicates how successful positive situations have been estimated:  
\[Recall=\frac{TP}{(TP+FN)}. \tag{14}\]  
Precision indicates the success for a situation estimated as positive:  
\[Precision=\frac{TP}{(TP+FP)}. \tag{15}\]  
F-Measure is the harmonic average of recall and precision, and it indicates the overall accuracy of classification:  
\[F-Measure=\frac{2*Precision*Recall}{(Precision+Recall)}. \tag{16}\]  
ROC curves, one of the performance measures in classification, are performance measures for the classification problem at various threshold values. ROC is a probability curve and the area under the curve (AUC) represents the degree or measure of separability. The higher the AUC is, the higher the performance in distinguishing the class of profiles will be.