## 1 Introduction  
Online social networks (OSNs) have gradually supplanted traditional media as the primary platforms for public information consumption and opinion expression [1, 2]. Research indicates that these global information dissemination channels attract widespread participation in policy and news discussions. Their real-time nature and vast user base significantly contribute to the discourse surrounding trending topics and sudden events [3, 4]. The proliferation of the internet and the rise of social networks, such as TikTok and Twitter, have not only facilitated the seamless communication, exchange, and dissemination of information but also provided covert means for illegal activities and cybercrimes. Exploiting the anonymity and virtuality of social media platforms, malicious actors employ metaphors, coded language, and other methods to communicate and disseminate information. By manipulating or altering the information carried by these networks, they can directly influence the thoughts of online audiences, thereby shaping their judgments on social events. Furthermore, the anonymity and global reach of these platforms enable perpetrators to engage in unlawful activities across multiple locations while amplifying their influence through the broadcasting features of social media. Fortunately, such behaviors typically exhibit a high degree of correlation and organization [5]. Leveraging technologies such as data mining and artificial intelligence to construct malicious user profiles can offer significant potential for proactive risk mitigation. User labeling, or user profiling, involves using a set of user attributes or behavioral features to create concise yet representative descriptions based on data and behavior analysis. These attributes may include age, gender, IP address, occupation, education level, interests, and social media activities. Such data can be transformed into actionable intelligence for law enforcement, enabling the identification of high-risk individuals and the early detection of potential criminal activities. For instance, analyzing account registration details alongside data traces from clicks, visits, and bookmarks can directly identify and evaluate anomalous users--one of the most direct applications of user profiling in investigations [6]. Additionally, profiling can uncover behavioral patterns and interaction dynamics within criminal groups, facilitating the identification of other members and potential targets, thereby enabling the control and guidance of group behaviors [7]. In practice, although social media platforms contain valuable data for identifying individuals and predicting criminal tendencies, the sparse and unstructured nature of this data poses significant challenges for effective extraction and classification. The fragmentation and high degree of unstructured data demand sophisticated approaches in data mining and behavioral prediction. Furthermore, although academic research has explored user profiling from various perspectives and to varying degrees, its application in the context of internet communities remains relatively limited. However, the data carried by social network users often do not directly correlate with their risk levels. Instead, the relationship between user characteristics and risk exists in a higher-dimensional vector space. While basic attribute-based profiling captures substantial user information, deeper insights into user behavior and the extraction of high-dimensional features are essential for addressing risks posed by malicious online users. In this study, we advance research into predicting the \"risky behaviors\" of users based on profiling. A deep user profiling model based on behavioral patterns is introduced guided by the 3 Cs principle, adapted from the credit assessment 5 Cs framework. Features are extracted from internet data based on these principles.