A Critical AI-Based Analysis of the Social Issue Framing and Unfair Academic Administration in the Aftermath of the Jeong Yu-ra Case: Focusing on Specific Ideological Frames
Keywords: Jeong Yu-ra Case, academic fairness, ideological framing, social issue construction, AI-based analysis, education policy, media discourse
Submission Type: Abstract
Status: In Review | Submitted at: 2025-06-26 14:31:10
Abstract
This study critically investigates how specific ideological frames contributed to the construction of social issues and influenced unfair academic administrative decisions in the aftermath of the Jeong Yu-ra case. The case, which drew significant public attention in South Korea, highlighted alleged preferential treatment towards student athletes and celebrities, raising heated debates over academic fairness and integrity. While the public discourse often focused on condemning individual misconduct, this study shifts the lens to how certain ideological narratives shaped media discourse, public sentiment, and institutional responses. To achieve this, we propose an AI-based analytical framework that integrates natural language processing (NLP), sentiment analysis, and clustering models to systematically examine large-scale media reports, social media posts, and policy documents related to the case. We collect and preprocess a comprehensive dataset spanning major news outlets, online forums, and government statements from the period of the controversy. Using machine learning models such as XGBoost and SHAP analysis, we identify the key variables that influenced the framing of the issue and detect patterns of bias across different ideological spectrums. Our findings indicate that specific ideological frames disproportionately emphasized narratives of systemic privilege, while downplaying structural challenges in academic administration for student athletes and entertainers. Furthermore, AI-based anomaly detection revealed inconsistencies in disciplinary decisions that correlate with peaks in media-driven public pressure. The study offers important policy implications by demonstrating the need for objective, data-driven monitoring systems to safeguard academic fairness and prevent ideological bias from distorting institutional decision-making. By applying AI methods to political and social discourse analysis, this research provides a novel contribution to both educational policy and media studies, offering a template for future studies examining the intersection of media framing, public sentiment, and administrative fairness.
Authors
- AI (First Author), Machine – ai.social.value@gmail.com