# Community Detection Framework Using Deep Learning in Social Media Analysis
## 2 Related Work
### Methods Based on Deep Learning  
In the context of social media analysis, community detection methods have been applied to study the graph topology of developing brains, which are characterized by noise and heterogeneity [26]. Different approaches to community detection have emerged, including semi-supervised methods that incorporate various types of information, such as topology, embedding, and content [27]. Topology analysis algorithms play a crucial role in community detection, along with ranking, walking, path-finding, and link prediction algorithms [28]. However, existing methods may not fully utilize network topology information, leading to limitations in uncovering hidden community structures [29]. Studies have also explored the modularity and community structure in real-world networks, revealing unexpected structural features through statistical analyses [30]. However, focusing on topology analysis can only analyze the relationship between the directly connected nodes, which has significant limitations. Authors [31] point out that a more accurate online community detection result can be obtained by comprehensively analyzing user relationships and similarities between users, thus proposing a community detection method that integrates network structure and community content. In recent years, with the development of neural networks and GNNs [32], more and more research on community detection models using neural network algorithms. [PERSON]. [33] first proposed a GNN-based community detection model. The GNN has shown promise in various tasks [34], including node classification and link prediction, and has been adapted for community detection by incorporating both node features and graph topology. Since then, supervised [35] and unsupervised [36] methods have been proposed. The model ClusterNet, as proposed by [8], is a comprehensive approach that considers link prediction as a learning problem and community detection as an optimization problem. It achieves an optimal modularity of 0.55 for multiple community detection datasets. [PERSON] et al. [37], who proposed a dynamic community detection framework using graph CNNs and contrastive learning to capture the adjacent characteristics of communities in social networks. This study demonstrated the effectiveness of using attention mechanisms to capture the dynamic nature of community structures. These examples highlight the trend of leveraging deep learning techniques to handle the complexities of social media data. However, most of the performance were not evaluated on the Enron dataset and other unstructured dataset, and it does not incorporate textual content as a feature. Furthermore, pure topology-based approaches often overlook the context and semantics of interactions, which can lead to misidentification of communities that share similar structural patterns but differ in purpose or content. Also, some of the previous methods do not account for the dynamic nature of social media interactions, failing to adapt to evolving community structures over time. Our proposed integration with content analysis addresses these limitations by incorporating communication context and structural relationships. This approach allows for a more nuanced understanding of community dynamics, leading to more accurate identification of communities. Unsupervised community detection models for social network analysis are still limited. [PERSON] et al. [38] introduced LAROUSSE, a framework for detecting textual adversarial attacks, while [PERSON] et al. [39] presented ProSeCo, an unsupervised pretraining approach for object detection. [PERSON] et al. [40] explored self-supervised deep learning for unsupervised discovery in astronomical images, and [PERSON] et al. [41] developed a deep learning framework for anomaly detection in industrial data. These studies highlight the importance of unsupervised approaches in various domains and showcase the potential of deep learning frameworks for community detection tasks.