Community Detection Framework Using Deep Learning in Social Media Analysis

Abstract

Social media analysis aims to collect and analyze social media user information and communication content. When people communicate through messages, phone calls, emails, and social media platforms, they leave various records on their devices and the Internet, forming a huge social network. Community detection can help investigators analyze group leaders and community structure, which is significant to further crime control, identifying coordinated campaigns, and analyzing social network dynamics. This paper proposes the application of deep learning methods for community detection. Our main idea is to utilize social network topology and social network communication content to construct user features. The proposed end-to-end community detection framework is the implementation of Graph Convolution Network and can display the social network topology, locate the core members of the community, and show the connections between users. We evaluate our framework on the Enron email dataset. Experimental results indicate that our proposed model achieves a 1.1% higher modularity score than the unsupervised benchmark methods. We also concluded that the community detection framework should be able to analyze social networks, enabling investigators to reveal connections between people.