# Community Detection Framework Using Deep Learning in Social Media Analysis
## 1 Introduction  
Social media analysis involves the systematic examination of data obtained from social media platforms to gain insights into user behavior, societal trends, and community dynamics. By leveraging social media data, researchers and practitioners can derive actionable insights that inform decision making across diverse domains. Specially in cyber security, social media analysis is the scientific examination of data retrieved from digital devices to ensure the information is admissible in court [1]. As individuals communicate through various platforms, like messages, phone calls, emails, and social media, they leave behind digital records that form a massive social network. These records are invaluable for investigators, as they provide critical evidence in cases. Email, for example, is an essential mode of communication in daily life but is also commonly exploited in online criminal activities. However, current approaches often focus on surface-level metadata, such as sender and recipient information, without delving deeper into the network structures and relationships between individuals [2]. Investigating email content can reveal key details, like the sender's and recipient's identities, applications used, and timestamps, all of which are vital for legal investigations. This study addresses the gap by focusing not only on the extraction of this digital evidence but also on understanding the underlying community structures within these social networks, providing investigators with a more comprehensive view of criminal activity. A core task in social media analysis involves identifying individuals involved in a case, recognizing groups, and detecting key members of these groups [3]. In cases involving organized crime, social network and community detection research can illuminate ganghierarchies, including identifying the gang boss and the internal structure, which holds significant value for criminal investigations and control. Existing methods in social media analysis often focus solely on network topology--analyzing the structure of connections between individuals [4]--without incorporating the content of communications, which is a significant shortcoming in complex investigations [5]. Communication content provides insight into the nature of relationships and helps accurately detect community structures beyond just statistical properties and user connections. While existing methods can detect key members or groups based on a network structure, they typically overlook the actual content of the communications that occur within these networks. This study fills this gap by integrating both the network topology and the content of communications, allowing for a more nuanced analysis. GCN (Graph Convolutional Network)-based algorithm is utilized to effectively integrate both network topology and communication content and deeply understand the relationships between users. This combination enhances the accuracy of community detection, making it particularly valuable for investigations that require uncovering hidden structures or key individuals in complex social networks. By combining these two elements, the proposed framework helps investigators understand not only who is connected but also what is being communicated, providing critical insights into the nature of the relationships and potential criminal activities that may otherwise go undetected. This paper aims to develop a GCN-based community detection framework that integrates both social network topology and communication content for more accurate identification of communities. It seeks to provide law enforcement with a more efficient tool for analyzing large-scale social media data, enabling the detection of key individuals and hidden networks. By incorporating communication content, this study improves the precision of community detection in cybercrime investigations. The framework is validated using real-world datasets, with its performance evaluated through metrics like the modularity index.