## 4 Experiment
### Stacking Base Classifier Performance Analysis  
To analyze the effectiveness of the five base classifiers in deriving high-dimensional features, \(\chi^{2}\) Chi-squared, \(\chi^{2}\)\(p\)-value, and Pearson and Spearman correlation coefficients are used. These correlation metrics help quantify the strength of the relationships between features and labels, focusing on absolute values. The evaluation is performed on both the balanced and imbalanced datasets, comparing the performance of different attribute features using GBDT as the classifier. The GBDT model utilizes 100 decision trees with a learning rate of 0.1 and a maximum tree depth of 3, and evaluation is based on precision, recall, and F1-score metrics. The test and correlation analysis results for the high-dimensional features extracted from population attributes are presented in Table 5, with prediction results for spam and non-spam information shown in Table 6. As indicated in Table 5, all the high-dimensional features derived from the user statistics pass the significance tests, confirming that, despite variations in the influence of each feature on the profiling results, they all contribute positively to improving accuracy. The high-dimensional features generated by different base learners are strongly correlated with the sample labels, with decision trees yielding the highest correlation. However, as shown in Table 6, decision tree-derived high-dimensional features exhibit balanced but mediocre prediction results. For non-spam information, the precision metric is of primary concern, while for spam, recall is prioritized. Classifier 2 demonstrates high precision for predicting positive samples, meaning that most of the cases it identified as positive are indeed correct, reducing false positives. Additionally, it shows high recall for predicting negative samples, meaning it successfully captures most of the actual negative samples, reducing false negatives. Therefore, classifier 2 (SVM) is chosen as the base learner for user attribute features in this study. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Label** & **Classification** & **Count** & **Percent** \\ \hline \multirow{3}{*}{Spam} & Local marketer & 1063 & 15.1\% \\  & Polluter & 1716 & 24.2\% \\  & Bad-mouther & 759 & 10.7\% \\ Non-spam & Non-spammer & 3538 & 50\% \\ \hline Total & & 7076 & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Sample distribution of the Apontador dataset. The test results and correlation analysis for the high-dimensional features extracted from tweet attributes are shown in Table 7. All the high-dimensional features extracted from tweet attributes pass the significance tests, but different base learners exhibit varied performance. Specifically, 1D-CNN, MLP, and decision trees perform better than Logistic learners in extracting high-dimensional features from tweet attributes, which differs from the results observed for population attributes. In the correlation analysis of the high-dimensional features extracted from tweet attributes, decision trees still achieve the best correlation metrics. However, their correlation coefficients do not differ significantly from those of other base classifiers. The high-dimensional features extracted by MLP show nearly identical Pearson and Spearman correlation coefficients to those extracted by decision trees. The classification results for spam and non-spam information using high-dimensional features from tweet attributes as input are presented in Table 8. MLP achieves the highest precision score (0.824) and recall score (0.823) with marginal differences. The F1 score also indicates that MLP performs more consistently in predicting non-spam information. In terms of balancing false positives and false negatives, MLP demonstrates strong performance. Its high precision reduces the misclassification of positive samples, while its high recall ensures that nearly all negative samples are correctly identified. Consequently, MLP is selected as the base learner for high-dimensional feature extraction from tweet attributes.