## 7 Analysis of X Cyber Threat Solutions
Recall is defined as the proportion of actual positives that the model successfully identifies and is positively correlated with the number of correct predictions from the minority class. Precision, meanwhile, measures the effectiveness of the classifier in predicting positive outcomes when it expects a positive result. The F1-score combines both precision and recall into a single metric, representing their harmonic meaning. This metric is particularly useful for evaluating the performance of models on minority classes and is widely used in information retrieval systems. The Area Under the Receiver Operating Characteristic Curve (AUC-ROC) is another important metric, especially in cases of unbalanced data. The AUC-ROC allows for the assessment of model performance by considering the trade-off between true positive rates and false positive rates. The F1-score can also be employed to evaluate the training examples by integrating both precision and recall. Additionally, a curve illustrating the relationship between recall and false positive rate can be plotted, with better model performance correlating with a higher AUC value. Further details on calculating and interpreting PR curves are provided by [166]. In this study, precision, recall, and F1-score were used to assess classifier performance, with the specific formulas shown in Table 3.  
In this section, a detailed comparison is presented for the studies previously introduced in the section on security content detection: SYNAPSE, DeepNN, DataFreq, CyberX [144], text mining, and SONAR [147]. The reason why a detailed comparison is presented for six studies previously introduced is to provide a comprehensive analysis of the similarities and differences between the studies. This comparison may help to identify patterns, trends, and gaps in the existing research, as well as to determine which approaches or methodologies have been most effective in addressing the research questions. Presenting a detailed comparison of the six studies is a valuable approach to gaining a deeper understanding \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & **Description** & **Equation** & **Range** \\ \hline Accuracy (A) & Assess the number of TPs & \(\text{A}=\frac{\text{TN}+\text{TP}}{\text{TN}+\text{FN}+\text{TP}+\text{FP}}\) & [0–1] \\ \hline Recall & The ratio of TP to a TP and FN & \(\text{R}=\frac{\text{TP}}{\text{TP}+\text{FN}}\) & [0–1] \\ \hline Precision & The ratio of TP to a TP and FP & \(\text{P}=\frac{\text{TP}}{\text{TP}+\text{FP}}\) & [0–1] \\ \hline F1-Score & Combines precision and recall & \(\text{FI}=2\frac{\text{P}-\text{R}}{\text{P}+\text{R}}\) & [0–1] \\ \hline AUC & The area between two points bounded by the function and the x-axis & \(\text{AUC}=\int_{\text{a}}^{\text{b}}\text{f}(\text{x})\text{d}\text{x}\) & [0–1] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance measures. of the existing research and informing future research directions. These studies were chosen to meet the objectives of analyzing security content on X without relying on specific vulnerabilities. Various prediction factors were compared, and their measurements were combined to provide an overall assessment and recommendation for real-time usage. The study also provides insight for future research aimed at improving models to increase the probability of cybersecurity success. The prediction factors assessed included scope of detecting the investigation of semantic characteristics, the complexity of the algorithm, degree of information condensation, scalability, and effectiveness. The results for each factor were summarized for all methods, revealing interesting comparison figures. The evaluation of the factors was further analyzed to provide the pros and cons of each model for further investigation and combination.