## 3 Method
### High-Dimensional Feature Extraction Using Stacking Learning  
Although basic user attributes provide significant insights, the relationship between social network users' features and their risk levels is often not directly apparent. Instead, this correlation exists in a higher-dimensional space. To further explore user behaviors and uncover hidden features, the extraction of deep, high-dimensional attributes becomes essential.  
To combine the predictions of two or more base machine learning algorithms, an ensemble learning-based approach is employed for the design of feature extractors. Classic ensemble learning structures include bagging, boosting, and stacking. The goal of bagging is to enhance the model's generalization ability by reducing the variance of the base classifiers. Therefore, the effectiveness of bagging largely depends on the stability of the base classifiers. In the case of boosting, from a mathematical perspective, it can reduce the bias of the results, theoretically enabling it to generate arbitrarily precise classifiers, even with weak base learners. However, this also makes boosting highly sensitive to noise, making it difficult to achieve good training results in datasets that are not conducive to it. Additionally, the serial structure of boosting leads to slower training times. On the other hand, stacking's training method improves both model variance and bias. While aiming for broad model generalization, it retains the original features, with its meta-classifier used to find the corresponding weights or combinations of the features from the updated training set. If the outputs of the base classifiers are regarded as deep features of the samples, the meta-model can also be described as a multimodal data fusion model with deep information modalities.  
After comparing and contrasting the three ensemble learning approaches, we finally select the stacking model as the overall framework for the proposed model. Based on the analysis in Section 3.1, the attributes are categorized into three groups: population attributes, social attributes, and tweet attributes. Each group represents unique dimensions of user behavior, requiring specific analytical approaches. The workflow, illustrated in Figure 1, integrates these groups using a tailored stacking approach.  
In this method, base classifiers are trained separately for each attribute group, with only the best-performing classifiers retained. These are then integrated by a meta-classifier, which combines the output of the base classifiers into a single prediction. The ensemble model optimizes overall performance by considering the combined strength of all classifiers rather than relying on individual models. This stru  
Figure 1: Workflow of improved stacking ensemble learning.  
that each data type is analyzed effectively while capturing their deeper relationships in high-dimensional space.  
To complement the foundational attributes, we introduce high-dimensional attributes in this paper, derived from a combination of the three attribute groups and advanced feature extraction methods. These new attributes, outlined in Table 3, encapsulate user characteristics that may not be evident in low-dimensional features but can be identified through advanced machine learning techniques.  
For feature extraction, several supervised learning algorithms are employed, including logistic regression, support vector machines (SVMs), multi-layer perceptrons (MLPs), 1D convolutional neural networks (1D-CNNs), and decision trees. These algorithms process the basic features to generate higher-order representations that can reveal latent patterns linked to user risk. The results of these models will be evaluated in Section 4 to determine their effectiveness in uncovering meaningful high-dimensional attributes. This stacking-based framework effectively bridges the gap between basic user features and complex, high-dimensional patterns, providing a robust foundation for understanding user behavior and predicting risk on social networks.