본문 바로가기
KERD
논문KCI 등재

분노, 비판, 대립의 장이 되었던 트위터 - 토픽모델링을 이용한 1029 이태원 참사 트윗 분석 -

Twitter As a Sphere for Anger, Criticism, and Political Confrontation - An Analysis of Tweets Regarding 10·29 Itaewon Disaster Using DMR Topic Model -

0

조회

0

다운로드

1

인용

0

참고문헌

키워드

초록

이 연구는 10·29 이태원 참사와 관련된 트윗을 대량으로 수집하고 정제한 후 DMR 토픽모델링으로분석하였다. 모델 검증을 통해 가장 적절한 토픽의 수로 15개를 설정하였다. 총 15개 토픽을 클러스터링하여 정보 반응, 감정 반응, 정부 비판 반응. 정치적 대립 반응, 2차 피해 반응, 공감 반응 및기타 등 7개 그룹으로 묶을 수 있었다. 토픽의 분포를 시계열적으로도 살펴보았는데, ‘대책 미비’, ‘책임’, ‘애도와 추모’ 토픽의 비중은 시간이 지날수록 감소한 반면, ‘막말 논란’, ‘분노와 비난’은시간이 지날수록 증가하였다. 이와 함께 전체 기간을 3개의 시기로 나누어 토픽의 차이를 살펴본결과 시기별로 토픽 비중의 차이가 나타났는데 ‘애도기’와 비교해 볼 때 ‘수사기’와 ‘국정 조사기’에서는 ‘수사와 구속’, ‘진상 규명’ 토픽의 비중이 증가하는 것을 볼 수 있었다. 이러한 토픽의 구조와토픽의 시간별 변화에 따른 분석 결과에 기반하여 재난의 정치화와 공동체의 회복에 관점에서연구 결과의 함의를 논의하였다.

Abstract

This research involved the extensive collection and refinement of tweets related to the 10·29 Itaewon Tragedy, followed by analysis using DMR topic modeling. Through model validation, the optimal number of topics was determined to be 15, clustered into seven groups: informational, emotional, government criticism, political confrontation, secondary harm, empathetic responses, and miscellaneous. Examining the temporal distribution of topics revealed a decrease in the weight of topics such as 'Lack of government preparedness,' 'Accountability for the disaster,' and 'Condolences and memorials for victims' over time, while topics like 'Controversy over disrespect for victims' families' and 'Anger and Blame' increased. Further, dividing the overall period into three phases demonstrated variations in topic weights. Compared to the 'Mourning' phase, the 'Investigation' and 'Parliamentary Inquiry' phases indicated an increase in the weight of topics related to 'Investigation and detainment of those responsible' and 'Demand for the truth about the disaster.' The research concludes by discussing the implications for the politicization of disasters and community recovery.

참고문헌 (35)

  1. Acheampong, F. A., "Transformer Models for Text-based Emotion Detection : A Review of BERT-based Approaches", Artificial Intelligence Review, pp.1~41 (2021)
  2. Ahmed, A., "Analysis of Post-crisis Twitter Communication: A Study of the Iquique, Chile Earthquake", Proceedings of the Australasian Conference on Information Systems ACIS (2014)
  3. Alam, F., "A Twitter Tale of Three Hurricanes: Harvey, Irma, and Maria", ArXiv Preprint ArXiv: 1805.05144, ~ (2018)
  4. Barberá, P., "Tweeting From Left to Right: Is Online Political Communication More than an Echo Chamber?", Psychological Science, 26(10), pp.1531~1542 (2015)
  5. Bird, D., "Flooding Facebook-the Use of Social Media during the Queensland and Victorian Floods", Australian Journal of Emergency Management, 27(1), pp.27~33 (2012)
  6. Blei, D. M., "Latent Dirichlet Allocation", Journal of Machine Learning Research, 3, pp.993~1022 (2003)[DOI]
  7. Druckman, J. N., "What do We Measure When We Measure Affective Polarization?", Public Opinion Quarterly, 83(1), pp.114~122 (2019)
  8. Duncombe, C., "The Politics of Twitter : Emotions and the Power of Social Media", International Political Sociology, 13(4), pp.409~429 (2019)
  9. Ehnis, C., "Social Media in Disaster Response: Queensland Police Service-Public Engagement during the 2011 Floods", ACIS 2012: Location, location, location: Proceedings of the 23rd Australasian Conference on Information Systems 2012, pp.1~10 (2012), ACIS
  10. Griffiths, T. L., "Finding Scientific Topics", Proceedings of the National Academy of Sciences, 101(suppl_1), pp.5228~5235 (2004)
  11. Guggenheim, M., "Introduction : Disasters as Politicspolitics as Disasters", The Sociological Review, 62(1_suppl), pp.1~16 (2014)[DOI]
  12. 함승경, "2003년∼2020년 한국 일간지의 ‘위안부’ 보도 분석 : 빅데이터를 이용한 의제 분석을 중심으로", 한국언론정보학보, (111), pp.181~215 (2022), 한국언론정보학회
  13. 허고은, "토픽 모델링 기반 과학적 지식의 불확실성의 흐름에 관한 연구", 정보관리학회지, 36(1), pp.191~213 (2019), 한국정보관리학회[DOI]
  14. Houston, J. B., "Social Media and Disasters : A Functional Framework for Social Media Use in Disaster Planning, Response, and Research", Disasters, 39(1), pp.1~22 (2015)
  15. Jang Hyun Kim, "Relation Between News Topics and Variations in Pharmaceutical Indices During COVID-19 Using a Generalized Dirichlet-Multinomial Regression (g-DMR) Model", KSII Transactions on Internet and Information Systems, 15(5), pp.1630~1648 (2021), 한국인터넷정보학회[DOI]
  16. 김진환, "토픽모델링을 이용한 영업 분야 연구 동향 분석", 마케팅연구, 36(1), pp.99~120 (2021), 한국마케팅학회[DOI]
  17. Kim, Minwoo, "What Rather than How : A DMR Topic Modeling Analysis of News Coverage on the British Museum", 2023 IEEE International Conference on Big Data and Smart Computing (BigComp), pp.167~173 (2023)
  18. Kim, Seoyoung, "How South Korean Internet Users Experienced the Impacts of the COVID-19 Pandemic : Discourse on Instagram", Humanities and Social Sciences Communications, 9(1), pp.1~12 (2022)
  19. 김영욱, "From Hating Places to Hating People: A Topic Model Analysis of Hate-Related Discourses in South Korean News Reports", 커뮤니케이션학 연구, 28(4), pp.5~27 (2020), 한국커뮤니케이션학회[DOI]
  20. Landwehr, P. M, "Data Mining and Knowledge Discovery for Big Data: Methodologies. Challenge and Opportunities", pp.225~257 (2014)
  21. Lee, Junbum, "KcBERT : Korean Comments BERT", Proceedings of the 32nd Conference on Korean Language and Information Processing, pp.437~440 (2020)
  22. Malhotra, N., "Attributing Blame : The Public's Response to Hurricane Katrina", The Journal of Politics, 70(1), pp.120~135 (2008)
  23. Mimno, D., "Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression", ArXiv Preprint ArXiv: 1206.3278, ~ (2012)
  24. Mimno, D., "Optimizing Semantic Conference in Topic Models", Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp.262~272 (2011)
  25. Mirbabaie, M., "Communication Roles in Public Events", Working Conference on Information Systems and Organizations, pp.207~218 (2014), Springer
  26. Mukkamala, A., "Enhancing Disaster Management through Social Media Analytics to Develop Situation Awareness what Can be Learned From Twitter Messages about Hurricane Sandy?", the Pacific Asia Conference on Information Systems (PACIS) AIS Electronic Library (AISeL), pp.165~ (2016)
  27. Mukkamala, A. M., "The Role of Social Media for Collective Behaviour Development in Response to Natural Disasters", 26th European Conference on Information Systems. AIS Electronic Library (AISeL), pp.1~18 (2018)
  28. Park, Sanghyun, "Semantic Network Analysis of President’s Communication and Social Media Public’s Comments during a National Crisis : Focusing on the Sinking of Sewol Ferry and Facebook of the Blue House", East-West Channel, 37, pp.43~73 (2015)
  29. Röder, M., "Exploring the Space of Topic Conference Measures", Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, pp.399~408 (2015)
  30. Schofield, A., "Comparing Apples to Apple : The Effects of Stemmers on Topic Models", Transactions of the Association for Computational Linguistics, 4, pp.287~300 (2016)
  31. Shi, P., "Simple Bert Models for Relation Extraction and Semantic Role Labeling", arXiv preprint arXiv: 1904.05255, ~ (2019)
  32. Song, Min, "Analyzing the Political Landscape of 2012 Korean Presidential Election in Twitter", IEEE Intelligent Systems, 29(2), pp.18~26 (2014)
  33. Takahashi, B., "Communicating on Twitter during a Disaster : An Analysis of Tweets during Typhoon Haiyan in the Philippines", Computers in Human Behavior, 50, pp.392~398 (2015)
  34. Tyagi, A., "Affective Polarization in Online Climate Change Discourse on Twitter", 2020 IEEE, 2020, 443~447
  35. Wörner, J., "Comparison of Topic Modeling Techniques in Marketing Results From an Analysis of Distinctive Use Cases", ECIS 2021Proceedings, pp.1~18 (2021)

관련 논문AI추천

이 간행물의 다른 논문 (11건)