텍스트 마이닝을 활용한 마약중독 관련 뉴스 기사 분석
Analysis of News Articles Related to Drug Addiction Using Text Mining
0
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
본 연구는 국내 마약중독 관련 뉴스 기사에서 나타난 핵심 주제와 시기별 변화 경향성을 살펴보기 위해 빅카인즈에서 2010년부터 2023년까지 526건의 뉴스 기사를 수집하였다. 텍스트마이닝 기법으로 핵심 키워드와 시계열, 토픽모델링 분석 후 시각화하였다. 그 결과는 다음과 같다. 첫째, ‘경찰’, ‘재판’, ‘처벌’, ‘마약범죄’, ‘구속’ 등이 핵심 주제어로 나타났다. 둘째, 시기별 언론 기사 빈도 변화는 3기와 4기에 가파른 상승세를 보였다. 마약류 중독 관련 대형 사건과 사회적 문제, 정책 변화가 있는 연도의 경우 기사 건수가 증가하였다. 셋째, 토픽모델링을 통한 대주제는 ‘해외 마약류 중독’, ‘엄벌주의적 형사처분’, ‘회복지원 정책’, ‘마약중독 예방 활동’, ‘마약중독자 회복 활동’로 분류되었다. 연구를 통해 ‘효과적인 치료와 재활’, ‘예방 교육 강화’, ‘중독자의 회복 활동’이 확대되고 있는 중요 주제로 확인되었다. 이를 바탕으로 향후 연구를 위한 제언과 한계점에 대해 논의하였다.
Abstract
This study collected 526 news articles from BigKinds from 2010 to 2023 to examine the core topics and trends of change over time in domestic drug addiction-related news articles. The text mining technique was used to analyze core keywords, time series, and topic modeling, and then visualized. First, ‘police’, ‘trial’, ‘punishment’, ‘drug crime’, and ‘arrest’ appeared as key topics. Second, the frequency of media articles by period showed a steep increase in the 3rd and 4th periods. In years with major drug addiction-related incidents, social problems, and policy changes, the number of articles increased. Third, major topics through topic modeling were classified as ‘overseas drug addiction’, ‘strictly punitive criminal disposition’, ‘recovery support policy’, ‘drug addiction prevention activities’, and ‘drug addict recovery activities’. Through research, effective treatment and rehabilitation, strengthening prevention education, and recovery activities for addicts were identified as important topics that are expanding. Based on this, suggestions for future research and limitations were discussed.
참고문헌 (32)
- Drug Crime White Paper 2023, "Supreme Prosecutors' Office Republic of Korea", ~ (2024)
- S. Park, "Analysis of the Drug Problem through Domestic and Foreign Drug Harm Index Development", Public policy review, 33(2), pp.209~227 (2019)
- Lancaster K, "Illicit drugs and the media : models of media effects for use in drug policy research", Drug Alcohol Rev, 30(4), pp.397~402 (2011)
- "The financial news"[URL]
- "Hankooki.com"[URL]
- "Ministry of Food and Drug Safety"[URL]
- S. Park, "A Study on Change in Media Reports on Gambling Addiction-Focusing on Comparison Between Those in 2005-2009 and 2015-2019", The Journal of Humanities and Social science, 11(6), pp.2845~2857 (2020)
- Boykoff, M. T, "Climate Change Science and Policy", pp.397~404 (2009), Island Press
- Shoemaker, P. J., "Mediating the message in the 21st entury: A media sociology perspective" (2013), Routledge
- Maxwell E. McCombs, "The Agenda-Setting Function of Mass Media", The Public Opinion Quarterly, 36(2), pp.176~187 (1972)
- H. Kim, "Topic Modeling of Media Reports on Smartphone Addiction : A Study on the Comparison of Government Policies between 2010 and 2018", Korean Journal of Broadcasting &Telecommunications Research, (104), pp.38~69 (2018)
- Y. Chang, "An Analysis of Characteristics of Online Articles Related to Youth Cyber Gambling through Keyword Network Analysis", Journal of Future Oriented Youth Society, 21(1), pp.123~144 (2024)
- S. Jang, "Youth Gambling Keywords Analysis Using Social Big Data", The Journal of Humanities and Social science, 14(1), pp.517~531 (2023)
- S. Choi, "A Study on Change in News on Gambling Addiction-COVID-19 before and after Comparison", Korean Association of Addiction Crime Review, 12(4), pp.195~210 (2022)
- D. Lee, "A Study on the COVID-19Before and After Trends of Problematic Smartphone Use through Text Mining", The Journal of Social Science, 6(1), pp.65~82 (2021)
- J. Yang, "A Study on the Perception of Adolescent Smartphone Overdependence Using Big Data", Asia-pacific Journal or Convergent Recearch Interchange, 9(5), pp.129~138 (2023)
- H. Kim, "Topic Modeling of Media Reports on Smartphone Addiction", Korean Journal of Broadcasting & Telecommunications Research, (4), pp.38~69 (2018)
- J. Lee, "Time Series Analysis of the Correlation Between the Number of Drug Crime Arrests and Media Coverage for Enhancing Police Response to Drug Crimes", Journal of Industrial Convergence, 21(6), pp.13~21 (2023)
- M. Kim, "Analysis of Issues Related to Adolescents and Drugs after the COVID-19 Outbreak Using Topic Modeling", The Journal of the Korea Contents Association, 23(7), pp.457~464 (2023)
- Y. Kim, "The Trend Analysis of Drug-Related News Articles with Topic Modeling", Journal of the Korea Institute Of Information and Communication Engineering, 28(3), pp.356~359 (2024)
- H. Paek, "Topic Modeling and Semantic Network Analysis of Themes and Trends of News Reports on Drugs in South Korea", Health and Social Welfare Review, 44(2), pp.360~380 (2024)
- S. Lee, "A Study on the Research Trend of Gambling Addiction in Korea Using Semantic Network Analysis", Korean Association of Addiction Crime Review, 9(1), pp.45~71 (2019)
- World Drug Report 2005: Volumes I and II. UNODC. 2011, "United Nations Office on Drugs and Crime (UNODC)", ~ (2005)
- Drug Crime White Paper 2015, "Supreme Prosecutors' Office Republic of Korea", ~ (2016)
- Wiedemann G, "Opening up to Big Data : Computer-Assisted Analysis of Textual Data in Social Sciences", Forum Qualitative Social Research, 14(2), pp.311~320 (2013)
- S. Lee, "A Content Analysis of Journal Articles Using the Language Network Analysis Methods", Journal of the Korean Society for information Management, 31(4), pp.49~68 (2014)
- N. Kim, "An Analysis of Domestic Research Trends in Pastor's Spouses : Keyword Network Analysis and Topic Modelling to Identify", Journal of Creative Information Culture, 10(2), pp.75~87 (2024)
- W. Chung, "Big Data Text Analyses and Application:Using Netminer" (2022), Hakgisa
- S. Lee, "A Study on the Application of Topic Modeling for the Book Report Text", Journal of Korean Library and Information Science Society, 47(4), pp.95~114 (2016)
- S. Lee, "Network Anaiysis Methods" (2017), Nonyoungbook
- S. Shin, "Critical Discourse Analysis on Drug Addiction", The Journal of the Korea Contents Association, 22(9), pp.712~726 (2022)
- "Ministry of Justice"[URL]