텍스트 마이닝을 이용한 가상자산 규제 관련 언론 보도의 잠재적 프레임 분석

The aim of this study is to provide basic data collected from various cases of problems that may arise in a situation where regulations on virtual assets are not properly established. In particular, the media plays an important role as an interpreter to promote public understanding in the highly advanced field of the economy. Accordingly, by identifying potential frames related to domestic virtual asset regulation through text mining of newspaper articles, the study is to provide basic data to improve virtual asset regulation and to explore improvements in media reporting practices. Seven hundred articles were collected from Big Kinds, 117 key words were extracted using textome, and semantic network construction, CONCOR analysis, and density matrix analysis were performed using UCINET and Netdraw. In analysis, the organic connection of a total of five types of potential frames were identified: virtual asset investor protection, overseas regulations, government regulation discussions, money laundering damage, and petitions against regulations in domestic virtual asset regulation reports. The study also pointed out that the contents of the virtual asset regulation-related reports follow the practices pointed out as problems in the existing economic-related reports.