청소년 마약 및 중독 이슈 변화 분석: 뉴스 데이터 기반 DMR 토픽모델링을 통한 정책적 시사점

다국어 초록 (Multilingual Abstract)

This study analyzed big data using news data along with previous literature studies on youth drug problems to examine issues according to the temporal changes in youth drugs and derive policy implications for resolving them. To this end, DMR (Dirichlet Multinomial Regression) topic modeling was performed on 22,885 news articles from 103 media outlets collected by BigKinds from 1990 to 2025. The analysis results confirmed that youth drug problems were previously focused on one-off issues centered on legal crackdowns and punishments, but are gradually shifting to a new frame centered on treatment, rehabilitation, and prevention. In addition, youth mainly use Telegram, SNS, and the dark web to trade drugs, and changes in the digital environment have further accelerated the worsening of drug crimes. Therefore, in order to solve youth drug problems, efforts are needed to establish policies centered on treatment, rehabilitation, and prevention, establish youth drug specialized organizations, and connect with local communities in addition to crackdowns and punishments. It is hoped that this study will be used as data for effective policy establishment and basic research to solve youth drug problems.