Browse > Article
http://dx.doi.org/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  

Kim, Seongdeok (연세대학교 대학원 문헌정보학과)
Ahn, Yuri (연세대학교 대학원 문헌정보학과)
Park, Ji-Hong (연세대학교 대학원 문헌정보학과)
Publication Information
Journal of the Korean Society for Library and Information Science / v.55, no.2, 2021 , pp. 91-110 More about this Journal
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;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Lee, Sabin (2012). A Social Network Analysis of Politicians: Focusing on the Use of Twitter by the 18th lawmakers. Master's thesis, Seoul National University Graduate School, Korea.
2 Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2018). Analyzing Social Networks (2nd ed.). London: Sage.
3 Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
4 Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77-84.   DOI
5 Yeo, Yu-Jin & Kim, Seong-A (2020). The main contents and tasks of emergency support measures in response to Covid-19. Health.Welfare Issue & Focus, 382, 1-12.
6 Lee, Byeong-Kee (2020). A comparative analysis study of IFLA school library guidelines using semantic network analysis. Journal of Korean Library and Information Science Society, 51(2), 1-21.   DOI
7 Lee, Ji-Yeon, Jo, Hyun-Joo, & Yoon, Ji-Won (2014). Network analysis of Korean legislators using bipartite network projection. Journal of Internet Computing and Services, 15(4), 103-110.   DOI
8 Mihalcea, R. & Tarau, P. (2004). Textrank: bringing order into text. Proceedings of the 2004 conference on empirical methods in natural language processing, 404-411.
9 Bougouin, A., Boudin, F., & Daille, B. (2013). Topicrank: graph-based topic ranking for keyphrase extraction. International joint conference on natural language processing, 543-551.
10 Kim, H. & Park, H. W. (2007). Friendship networks amongst the 17th South Korean Assembly Legislators. Speech & communication, (8), 146-177.
11 Mimno, D. M. & McCallum, A. (2008). Topic models conditioned on arbitrary features with Dirichlet-multinomial regression. Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence, 24, 411-418.
12 Yang, S., Keller, F. B., & Zheng, L. (2016). Social Network Analysis: Methods and Examples. California: Sage Publications.
13 Jeong, Do-Heon & Jo, Hwang-Soo (2018). Discovering interdisciplinary convergence technologies using content analysis technique based on topic modeling. Journal of the Korean Society for information Management, 35(3), 77-100.   DOI
14 Bae, Jung-Hwan, Son, Ji-Eun, & Song, Min (2013). Analysis of twitter for 2012 South Korea presidential election by text mining techniques. Journal of Intelligence and Information Systems, 19(3), 141-156.   DOI
15 Kang, Myung-Koo (2000). A network analysis of political power structure. Journal of Communication Research, 37, 93-130.
16 Kang, Beom-Il, Song, Min, & Jho, Whasun (2013). A study on opinion mining of newspaper texts based on topic modeling. Journal of the Korean Society for Library and Information Science, 47(4), 315-334.   DOI
17 Kwahk, Kee-Young (2017). Social Network Analysis (2nd ed.). Seoul: Cheongram.
18 Kim, Jeong-A, Kim, Yong-Ho, & Kang, Myung-Koo (1994). A network analysis of korean political power structure in newspaper articles. Symposium and Seminar of the Korean Journalism Association, 31-59.
19 Kim, Hye-Young & Park, Ji-Hong (2020). A network analysis of the Library Bill Cosponsorship in the legislative process of the 19 th National Assembly of Korea. Journal of the Korean Society for Information Management, 37(2), 1-22.   DOI
20 Oh, Mi-Ae & Jun, Ji-Na (2020). Analysis of coronavirus disease-19 major issues based on social big data. Health.Welfare Issue & Focus, 376, 1-12.
21 Oh, Hyung-Geun (2020). Analysis of major social changes and information security issues after COVID-19. Communications of the Korean Institute of Information Scientists and Engineers, 38(9), 48-56.
22 Lee, Minchul & Kim, Hea-Jin (2018). Construction of event networks from large news data using text mining techniques. Journal of Intelligence and Information Systems, 24(1), 183-203.   DOI
23 Liu, G. Y., Hu, J. M., & Wang, H. L. (2012). A co-word analysis of digital library field in China. Scientometrics, 91(1), 203-217.   DOI
24 An, Ju-Young, Ahn, Kyu-Bin, & Song, Min (2016). Text mining driven content analysis of ebola on news media and scientific publications. Journal of the Korean Society for Library and Information Science, 50(2), 289-307.   DOI
25 Kim, Hyo-Dong (2013). A study on retweet networks of the 18th Korea Presidential Candidates. Journal of Political Communication, 31, 91-125.   DOI