# Detection of Inappropriate Tweets Linked to Fake Accounts on Twitter
## 3 Methodology
### Experimental Evaluation Criteria  
To evaluate the performance of the proposed system, the performance measurements are needed to assess how effective and efficient a detection algorithm or model is. The type of performance metric employed defines the nature of the information it provides about the performance of the models, algorithms, or processes. The effectiveness of the proposed model was assessed using a variety of performance indicators applied to the test data. For instance, a two-dimensional matrix with the first dimension indexed by actual zones (or class) and the second dimension indexed by the classifier's assigned zone (or class) was one of the performance measures. The main measure relies on two zones that are used for evaluating the confusion matrix: a positive (P) and a negative (N) zone associated with actual class and the detected class (see Table 1). The zones encompass: true positive (TP), false positives (FP), true negatives (TN), and false negatives (FN) and are all measures that are used to describe the make up of the matrix in this situation [33; 34].  
The practical definition of a TP is the metric that precisely detected inappropriate tweets associated to fake Twitter accounts. Whereas the erroneous detection of inappropriate tweets that are tied to fake Twitter accounts is an example of an FP, which can be operationally defined as a type 1 error. An FN is a measurement that, for operational purposes, is defined as the one that incorrectly disregards the detection of inappropriate tweets associated with fake Twitter accounts. This is known as a type 2 error. Finally, the operational definition of a TN is a measurement that precisely rejects the detection of inappropriate tweets that are tied to fake Twitter accounts.  
The accuracy of the detection of the inappropriate tweets linked to fake accounts on Twitter can be measured with the help of the confusion matrix in Table 1. Hence, the extent to which measurement detection of the inappropriate tweets linked to fake accounts on Twitter is in conformity with the actual value or reflects a defined standard is measured by the accuracy (A), which is the function of the detection operation associated with the error rate given as:  
\[\text{A}=100\%-\text{Error Rate}, \tag{1}\]  
where the error rate is defined as:  
\[\text{Error rate}=\frac{\text{detection rate}-\text{current rate}}{\text{current rate}}\times 100 \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Detected Class**} \\ \cline{3-4}  & & **P** & **N** \\ \hline \multirow{2}{*}{**Actual Class**} & **P** & TP & FN \\  & **N** & FP & TN \\ \hline \hline \end{tabular}
\end{table}
Table 1: The confusion matrix evaluation.  
Since the real detection rate as well as the current detection rate of the operation for identifying improper tweets linked with fake Twitter accounts were both deeply influenced, the actual accuracy of the detection rate can be expressed as  
\[\text{A}=\frac{\text{TP}+\text{TN}}{\text{P}+\text{N}}: \tag{3}\]  
The Equations (1)-(3) have been used to evaluate the performance of the proposed work. Following the application of the accuracies associated with the detection rate of the inappropriate tweets linked to fake accounts on Twitter, another measure of the detection is associated with \"Precision/sensitivity\", \"recall\", \"Sensitivity\", and \"Specificity\" of the measurement, where: sensitivity = TP/(TP + FN), specificity = True negative rate = TN/(TN + FP), and detection rate = TP/(TP + TN + FP + FN).