Use of Triangulation in Comparing the Blockchain Knowledge Structure between China and South Korea: Scientometric Network, Topic Modeling, and Prediction Technique

Abstract

Blockchain, as a new innovative technology, has become a popular topic in many fields in recent years. In this study, triangulation was used to investigate the development of knowledge structures. First, scientometric network analysis was employed to identify the cooperation of knowledge networks. It was found that the structure of blockchain knowledge networks in China is relatively more complex and diverse than in South Korea. Since increased teamwork in blockchain is conducive to the creation of high-quality knowledge products, the Chinese government appears to strongly promote diversified cooperation on blockchain technology through centralized policies. Second, machine-learning topic modeling was used to analyze the content exchanged via a collaborative network. As a result, it was found that both countries lacked the societal and commercial aspects of blockchain technology. Finally, we developed a prediction technique based on the Ernie model to automatically categorize the nature of blockchain research.

Decoding the Relationship of Artificial Intelligence, Advertising, and Generative Models

“…Unsurprisingly, machine learning reigns supreme, dominating centrality and occurrence metrics. This dominance underscores its foundational role as the very "backbone of optimized computer programs powering advanced AI applications", [54]. Its versatility extends to the advertising realm, where it personalizes content, optimizes targeting, and enhances overall effectiveness [55].…”