## 4 Experiment  
### Implementation Details  
The dataset used in this study is the publicly available Apontador dataset, which was collected from a location-based professional social network platform. The dataset was manually labeled by [PERSON] et al. (2018), with 3538 entries categorized as spam, divided into three classes: local marketer, polluter, and bad-mouther. Local marketer includes ads related to local businesses, where excessive advertisements and promotional content may interfere with users' normal browsing and some marketing information may contain phishing links or malware, posing security risks to users. Polluter refers to content that is irrelevant or unrelated to a specific location, and the proliferation of such content can lead to topic drift, disrupting the discussion atmosphere within the community. Bad-mouther is characterized by aggressive comments that target a particular place or other users, infringing upon their reputation and privacy and potentially causing psychological harm, thereby threatening social stability. These categories align with the malicious user labels discussed in the previous chapter, making the Apontador dataset suitable for experimental validation in this research. Additionally, 3538 non-spam entries are randomly selected to balance the dataset, resulting in a total of 7076 samples, as summarized in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature Name** & **Description** \\ \hline Population\_high & High-dimensional feature obtained by using various classifiers from population attributes \\ Tweet\_high & High-dimensional feature obtained by using various classifiers from tweet attributes \\ Social\_high & High-dimensional feature obtained by using various classifiers from social attributes \\ User\_high & High-dimensional feature obtained by using various classifiers from all attributes \\ \hline \hline \end{tabular}
\end{table}
Table 3: High-dimensional attributes of user profiles.  
The dataset contains a significant imbalance, with far fewer malicious users compared to regular users in reality. In order to evaluate the performance of different combinations more realistically, especially under threshold-sensitive evaluation metrics, a balanced dataset is used. On the other hand, to get closer to real life, the imbalanced dataset is used. This dataset reflects the actual distribution of high-risk and regular users. The imbalanced dataset is derived from the Apontador dataset, with 3538 non-spam entries (representing two-thirds) and 1769 spam entries (including 531 local marketing, 858 pollution, and 380 malicious defamation cases, representing one-third). In subsequent studies by [PERSON] et al., features are further categorized into four groups, totaling 60 normalized features. The grouping principles are consistent with those in this study, with slight differences in naming. For simplicity, content attributes and social attributes in the dataset are denoted as CA and SA, respectively, corresponding to the tweet attributes and social attributes in Table 4. User attributes and place attributes are combined into user attributes according to the principles of this paper and are simply referred to as UA, corresponding to the population attributes in Table 4.