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한국지능정보사회진흥원 (National Information Society Agency)
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온라인 공간의 정치 양극화는 심화될 것인가?: 선거 기사 댓글에 대한 경험적 분석

Is Political Polarization Reinforced in the Online World?: Empirical Findings of Comments about News Articles

  • 투고 : 2021.07.23
  • 심사 : 2021.08.18
  • 발행 : 2021.12.31
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초록

본 연구의 목적은 온라인 공간의 속성을 규명하고, 이러한 속성이 민주주의 운영에 미칠 영향을 경험적으로 분석하는 데 있다. 본 연구는 2021년 4월 7일 치러진 서울시장 및 부산시장 보궐선거에 관한 언론 기사와 댓글을 수집하여 온라인 공간의 속성과 정치 양극화를 경험적으로 분석하고 있다. 구체적으로 본 연구는 토픽모델링을 활용하여 보궐선거에 나타난 언론 보도의 다양성을 측정하였으며, 감성분석을 활용하여 기사 댓글에 비친 온라인 여론을 측정하였다. 이후 언론이 가장 주목한 보도가 온라인 여론에 영향을 미치는 여부를 단절적 시계열 분석을 통하여 분석하였다. 이러한 시도는 온라인 여론의 견고성을 검증하는 시도로써 정치 양극화의 수준을 측정하는 지표로 사용된다. 분석 결과를 보면, 첫째 언론은 보궐선거 지역과 후보에 따라 선거 관심도와 주제가 달랐다. 둘째, 언론 보도의 다양성에도 불구하고, 기사 댓글에 나타난 온라인 여론은 높은 부정 여론, 낮은 긍정 여론이 지속적으로 나타났다. 특히 선거일에 즈음할수록 양극화의 수준은 더욱 분명했다. 셋째, 단절적 시계열 분석 결과를 보면, 선거 관심도에 따라 정치 양극화의 변화 가능성이 차별적인 것으로 나타났다. 향후 온라인 공간을 통한 정치참여가 거부할 수 없는 흐름이란 점을 고려할 때, 본 연구는 온라인 공간에서 재현되는 정치 양극화 해소를 위한 방안 마련이 시급하다고 제언하고 있다.

The purpose of this research is to investigate the attributes of the online world and to analyze their influence on democracy. The research focuses on the mayoral by-elections that were held in Seoul and Busan, South Korea, on April 4, 2021. The study demonstrates the characteristics of online spaces and the polarization of the online public through news articles and user comments from the Internet. The research includes topic modeling to measure the diversity of media reports, sentiment analysis to measure online public opinion, and interrupted time series analysis to understand how a particular event influences online attitudes. A combination of these methods is used to attempt to estimate the strength of political polarity in the online environment. The study shows diverse media reports by election region and candidate, where the online public repeatedly reveals high negative and low positive attitudes towards each candidate. Moreover, political polarity can differ based on the level of interest in an election. Although voters pay less attention to a by-election than a presidential election, there is a solid political polarity in the online world. Hence, the research recommends preparing measures to alleviate the polarization as politics requires significant online participation.

키워드

참고문헌

  1. Kang, W. (2007). "How Does the Internet Transform Party Politics?" Informatization Policy, 14(2), 101-114.
  2. Kim, K. & Kim, K. (2006). "On Disputes over Political Participation Contributed by Electronic Democracy." Journal of Communication Science, 6(2), 43-75.
  3. Na, E., Lee, G. & Kim, H. (2009). "Social Implication of Reading/Writing Online Comments(Replies) in Representative Democracy: Internet News Comments(Replies), Political Trust, Media Trust, and Political Knowledge." Korean Journal of Journalism & Communication Studies, 53(1), 109-132.
  4. Norris, P. (2000). A Virtue Circle: Political Communication in Postindustrial Societies. Cambridge, UK: Cambridge University Press.
  5. Seo, J. & Kim, Y. (2014). "Social Movements and Political Parties in an Era of Social Media Income Inequality and the Occupy Movement in US." Korean Party Studies Review, 13(2), 101-129.
  6. Yang, S. (2008). "Case Study on Electronic Democracy in South Korea to Implement Democratic Electronic-Government." Journal of Korean Association for Regional Information Society, 11(3), 31-54.
  7. Eom, K. (2007). "Partisan Polarization, Electorate Polarization, and Political Participation." Korea and World Politics, 23(1), 121-152. https://doi.org/10.17331/kwp.2007.23.1.005
  8. Eom, K. (2018). Python for Political Science. Daegu: Kyungpook National University Press.
  9. Eom, K. (2020). "Whose Opinions are Represented in the Online World?" Journal of Future Politics, 10(1), 99-122. https://doi.org/10.20973/JOFP.2020.10.1.99
  10. Eom, K. & Kim, D. (2021). Automated Classification Model for Online Public Opinions in a Political Arena: KoBERT based Sentiment Analysis. Paper presented at Korean Association of Party Studies, July 8.
  11. Yoon, S. (2002). "A Critical Review of Online Political Participation Studies." Infomatization Policy, 15(3), 3-20.
  12. Yoon, S. & Chang, W. (2007). "Online Political Participation in S. Korea: Their Experience of the Netizen-Initiated Bottom up Model." Informatization Policy, 14(4), 82-101.
  13. Cho, S. & Chung, Y. (2006). "Cybercommunity, Information Access, and Political Participation: The Political Effects of Internet in the 17th National Assembly Election." Korea and World Politics, 22(2), 29-62.
  14. Jho, W. (2012). "[Online Citizen Participation] Local Governance and Communicative Administration through SNS." Monthly Public Policy, 75, February 2012, 24-26.
  15. Jhee, B., Shin, D. & Eom, K. (2013). "Exposure to Conservative Media and Issue Priority." Journal of Contemporary Politics, 6(2), 127-155.
  16. Hahn, K., Park. J., Lee, D. & Lee, H. (2013). "A Test of Representattiveness and Polarization in Twitter Followership: A Cross-national Assessment of Legislators Twitter Followers in the U.S. and South Korea." Journal of Cybercommunication Academic Sciety, 30(1), 295-336.
  17. Blei, D. (2012a). "Probabilistic Topic Models." Communication of the ACM, 55(4),77-84. https://doi.org/10.1145/2133806.2133826
  18. Blei, D. (2012b). "Topic Modeling and Digital Humanities." Journal of Digital Humanities, 2(1), 8-11.
  19. Fiorina, M., Abrams, S. & Pope, J. (2005). Culture War?: The Myth of a Polarized America. New York: Longman.
  20. Grimmer, J. & Stewart, B. (2013). "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts." Political Analysis, 21, 267-297. https://doi.org/10.1093/pan/mps028
  21. Kim, D. (2020). "How to Utilize Online Texts for Understanding Political Landscape - Method and Application of Online Public Opinion from Twitter". Ph.D. Dissertation, Department of Political Science, Kyungpook National University.
  22. Linden, A. (2015). "Conducting interrupted time-series analysis forsingle- and multiple-group comparisons." Stata Journal, 15(2), 480-500. https://doi.org/10.1177/1536867x1501500208
  23. Newman, D., Han Lau, J., Grieser, C. & Baldwin, T. (2010). "Automatic Evaluation of Topic Coherence." Paper presented at Human Language Technologies, The 2010 Annual Conference of the North American Chapter of the ACL, 100-108, Los Angeles, California.
  24. Park, Y., Jang, S., Lee, H. & Yang, G. (2018). "Divide in Ferguson: Social Media, Social Context, and Division." Social Media and Society. 4(3), 1-13.
  25. Yun, J. & Eom. K. (2015). "Social Networking Service and Political Attitude: An Experiment Using Facebook." 21st century Political Science Review, 25(3), 161-174. https://doi.org/10.17937/topsr.25.3.201510.161