## 4 Experiment
### Performance Evaluation of User Profiling with Integrated High-Dimensional Features  
Based on the analysis in Section 4.2, the final improved user profiling method, which extracts high-dimensional features using stacking-based ensemble learning, is illustrated in Figure 2. The first layer of the stacking model consists of multiple base learners, including logistic regression (Logistic), support vector machines (SVMs), and multi-layer perceptron (MLP), which are used to extract high-dimensional feature representations from different attributes. The high-dimensional features extracted from population attributes are denoted as _population_high_, those from tweet attributes as _tweet_high_, and those from all attributes as _user_high_\(1\), _user_high_\(2\), and _user_high_\(3\). These high-dimensional features are then concatenated with the dataset to form a new feature dataset. To achieve optimal identification performance, a composite neural network model with an attention mechanism is used for training, with its structure shown in Figure 3. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Base Learner** &
\begin{tabular}{c} **\(\chi^{2}\) Chi-** \\ **Squared** \\ \end{tabular} & \(\chi^{2}\)_p-Value_ & **Pearson** & **Spearman** \\ \hline
1 & Logistic & 2825.84 & 0 & 6.32 \(\times\) 10\({}^{-1}\) & 6.32 \(\times\) 10\({}^{-1}\) \\
2 & SVM & 833.34 & 3.03 \(\times\) 10\({}^{-183}\) & 3.44 \(\times\) 10\({}^{-1}\) & 3.44 \(\times\) 10\({}^{-1}\) \\
3 & MLP & 5983.71 & 0 & 9.20 \(\times\) 10\({}^{-1}\) & 9.20 \(\times\) 10\({}^{-1}\) \\
4 & 1D-CNN & 3355.86 & 0.28 & 6.89 \(\times\) 10\({}^{-1}\) & 6.89 \(\times\) 10\({}^{-1}\) \\
5 & Decision tree & 6260.92 & 0 & 9.41 \(\times\) 10\({}^{-1}\) & 9.41 \(\times\) 10\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Test results of the high-dimensional features extracted from user attributes. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Non-Spam**} & \multicolumn{3}{c}{**Spam**} \\ \hline
**No.** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline
1 & 92.1\% & 86.6\% & 89.3\% & 86.6\% & 92.8\% & 89.6\% \\
2 & 93.8\% & 85.4\% & 89.4\% & 86.1\% & 93.7\% & 89.7\% \\
3 & 90.8\% & 85.8\% & 88.2\% & 85.6\% & 91.8\% & 88.6\% \\
4 & 88.8\% & 84.1\% & 86.4\% & 84.7\% & 89.5\% & 87.0\% \\
5 & 85.2\% & 85.5\% & 85.3\% & 85.1\% & 85.1\% & 85.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Prediction results of spam and non-spam information when high-dimensional features of user attributes are input. The dataset is divided into training and test sets in an 8:2 ratio to produce the model's input \(X=[u_{1},\cdots,u_{s-1},u_{s}]^{T}\), where \(u_{i}(i=1,2,\ldots,s)\) represents the feature vector of sample \(i\), and \(s\) denotes the number of samples. CNN is used to extract features, with ReLU as the activation function in the convolutional layers and max-pooling applied. The final output \(O_{c}=[oc_{1},\cdots,oc_{s-1},oc_{s}]^{T}\), where \(oc_{i}(i=1,2,\ldots,s)\) represents each sample's output from the CNN network, is activated using a sigmoid function, following the parameter transmission formula detailed below:  
\[Z=ReLU(u\otimes W_{c}+b_{c}) \tag{1}\]  
\[P=max(Z)+b_{p} \tag{2}\]  
\[oc=sigmoid(P\otimes W_{o}+b_{o}) \tag{3}\]  
where \(Z\) is the output of the first convolution layer, \(P\) is the output of pooling, \(W_{c}\) is the weight of the convolution layer, \(W_{o}\) is the weight of the output layer, \(b_{c}\) is the convolution layer bias, \(b_{p}\) is the pooling layer bias, and \(b_{o}\) is the output layer bias. After convolution, LSTM is employed to extract text features. Its output \(h_{t}\) is as below:  
\[h_{t}=LSTM(oc_{t-1},oc_{t}),t\in[1,j] \tag{4}\]  
where \(oc_{t}\) is the value of \(oc\) at time \(t\), and \(j\) is the length of \(oc\). The output from LSTM is fed into an attention layer, where weights are assigned probabilistically.