Research Highlights
− Analyzed 1,736 articles from South Korea and U.S. media
− Used BERT model to predict public engagement (70% accuracy)
− Revealed distinct cultural differences in drug-related news coverage
Key Methods
− Computational text analysis
− Machine learning prediction models
− Cross-cultural comparative analysis
This study examines contrasting media framings of drug-related issues in South Korea and the United States (U.S.), emphasizing the need for culturally informed reporting that balances enforcement with health-oriented narratives. Using computational text analysis and machine learning (ML), 1,736 articles were analyzed to identify thematic differences, drivers of public engagement, and predictive models of audience interest.Results reveal South Korean media's predominant focus on enforcement and punitive measures, while U.S. media prioritize health and prevention. Public engagement in South Korea is driven by sensational elements, such as specific incidents and punitive measures. ML models, particularly BERT, achieved the highest accuracy in predicting audience responses, underscoring the potential of advanced computational approaches. This study offers actionable insights for ethical journalism, advocating for culturally nuanced, life-centered narratives that promote resolution and healing.
Keywords: Computational journalism, Machine learning, Media framing, News coverage, Public engagement, Public health communication
The following is the process for Research Question 2 (including data collection and preprocessing.