언론 빅데이터로 본 학교폭력 대응 정책의 대학입시 연계 및 리스크 전이 프로세스
University Admission Linkage and Risk Transfer Process of School Violence Countermeasure Policies: A Media Big Data Analysis
This study examines the structural transition of media discourse linking school violence to university admissions using news big data from 2000 to 2026. Analyzing 6,095 news articles through Semantic Network Analysis and Latent Dirichlet Allocation (LDA) topic modeling, the study traces shifts in core keywords and latent topics over time. The findings reveal that school violence discourse, which initially emphasized educational guidance and relationship restoration, has progressively moved toward mandatory student record entries, admission penalties, and judicial sanctions. Centrality analysis demonstrates that “admissions” functions as a critical mediating hub interconnecting diverse sub-discourses. Notably, aligned with major policy shifts-such as the mandatory inclusion of disciplinary records in college admissions starting in the 2026 academic year-media coverage of legal disputes, lawsuits, and procedural fairness expanded significantly. This research empirically illustrates how policy changes and media framing dynamically interact via policy feedback mechanisms, highlighting the systemic shift toward the judicialization of education. Ultimately, the study offers practical insights for anticipating legal risks and refining higher education admission guidelines.
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Abstract 1. 서론 2. 이론적 배경 2.1 공공 정책 프로젝트의 전개 과정과 외부리스크 전이 메커니즘 2.2 미디어 의제설정을 통한 이해관계자 커뮤니케이션과 프로젝트 피드백 프레임 3. 연구 방법 및 설계 3.1 분석 방법론: 텍스트 마이닝 및 LDA 토픽 모델링의 타당성 3.2 데이터 수집 및 전처리 3.3 분석 절차 및 방법 4. 연구 결과 분석 4.1 단어 빈도 분석 4.2 의미 연결망 및 중심성 분석 4.3 LDA 토픽 모델링 4.4 트렌드 분석 4.5 소결 및 논의 5. 결론 및 제언 5.1 결론 5.2 정책적 제언 5.3 연구의 한계 및 향후 과제 References