Mapping metaverse industrial architecture using LDA and bibliometrics based on technology news framing

Abstract

Enabling the public to grasp the state and trends of emerging technologies facilitates technology adoption, socioeconomic investment, and business growth. Science journalism is crucial in connecting the scientific community with the public. This study focuses on the metaverse due to its rapid expansion. To validate knowledge extraction, we analyze relevant metaverse news from TechCrunch.com, covering 2020 to 2023, to build the domain knowledge schema. This study introduces a novel approach that combines Latent Dirichlet Allocation (LDA) topic modeling with bibliometrics as a computational intelligent method to discover topics and construct knowledge based on technology news framing. LDA is used to identify the topics in metaverse news, while a bibliometrics method, i.e., co-word networks analysis, clarifies term association strength and visualizes the findings. The study outlines the seven key elements highlighted in technology journalism, which shape public perception and expectations of new technologies. These elements offer insights into the factors influencing the development, diffusion, and adoption of new technologies. The extracted representative terms and enterprises from eight topics help map the knowledge architecture diagram for the domain. The research contributes to helping stakeholders systematically understand metaverse technology topics and demonstrate collaborative partnerships between enterprises.

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