Communication and Influence of Traditional Culture Based on Social Network Analysis

Abstract

Social media significantly impacts traditional cultures, yet existing methods struggle to capture the delicate complexities of online cultural discussions. This lack of depth limits understanding of how traditions evolve and adapt in the digital age. To address this, CaCCGAN: Social Culture Analysis, a framework leveraging social network analysis (SNA) is proposed. Content-Aware Cycle-Consistent Generative Adversarial Network (CaCCGAN) explores communication and influence dynamics surrounding traditional culture on social media platforms. CaCCGAN gathers real-time data on traditional culture from Weibo, a prominent Chinese platform, focusing on relevant keywords. By employing Term Frequency-Inverse Document Frequency (TF-IDF) and a Binary Waterwheel Plant Optimization Algorithm (BWPOA), CaCCGAN identifies user-tradition relationships, influential users, and online communities. Furthermore, the CaCCGAN model analyzes communication styles and generates content reflecting these relationships. Analyzing this generated content offers valuable insights for stakeholders interested in cultural preservation, targeted marketing campaigns, and content creation strategies. The proposed CaCCGAN: Social Culture Analysis model attains 25.23%, 21.06% and 55.11% higher Precision value and 29.096%, 41.64% and 33.33% higher F1 Score value compared with the existing method such as PA-BiLSTM Neural Encoding for Cultural Adaptation and Emotion Analysis in Modern Media Communication (PA-BiLSTM), MFCSNet: Modeling Musician-Follower Dynamics in a Complex Social Network to Measure Musical Influence (MFCSNet) and Utilizing Social Media Data Analytics to Integrate Traditional Culture Through Digital Media Interaction and Dissemination (I-RankClus-WOA) respectively. This proposed CaCCGAN: Social Culture Analysis model presents a promising approach to understanding the evolving dynamics of communication and influence surrounding traditional culture online.