코로나19에 관한 국회의원 의정활동 네트워크 분석 - 신문 기사를 중심으로 - (A Social Network Analysis of Legislators’ Activities on COVID-19 in the National Assembly: Based on News Articles)

김성덕 (Seongdeok Kim); 안유리 (Yuri Ahn); 박지홍 (Ji-Hong Park)

doi:10.4275/KSLIS.2021.55.2.091

A Social Network Analysis of Legislators’ Activities on COVID-19 in the National Assembly: Based on News Articles

Journal of the Korean Society for Library and Information Science
2021, 55(2), pp.91~110
DOI : 10.4275/KSLIS.2021.55.2.091
Publisher : 한국문헌정보학회
Research Area : Interdisciplinary Studies > Library and Information Science
Received : April 26, 2021
Accepted : May 26, 2021
Published : May 31, 2021

Seongdeok Kim ¹, Yuri Ahn ¹, Ji-Hong Park ²

¹연세대학교 대학원 문헌정보학과
²연세대학교

Excellent Accredited

ABSTRACT

In the face of the prolonged Covid-19, this study conducted a network analysis to propose the policy direction for the Korean National Assembly to go forward. Using COVID-19 news articles, various types of networks were created and analyzed for the parliamentary activities of the Korean National Assembly related to Covid-19. Specifically, we utilize the co-occurrence and keyword information to generate two types of parliamentary networks: co-occurrence-based network and content-based network. In addition, a topic keyword-driven parliamentary network was constructed by using topic modeling. The results of the study are as follows. First, lawmakers in the ruling party had a wide range of topics regarding Covid-19, while lawmakers from other political parties had a limited number of issues covered. Next, a few representative legislators were identified as influential actors in most of the centrality indicators. Based on the research results, cooperation on diverse agendas related to Covid-19 should be promoted between lawmakers from various political parties. And representative legislators from both major parties should play a crucial role as intermediaries to increase communication between them.

KEYWORDS

COVID-19, legislators, social network analysis, centrality, topic modeling

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This paper was written with support from the National Research Foundation of Korea.

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