Analyzing media bias in defense and foreign affairs: A deep learning and eXplainable artificial intelligence approach

Abstract

This study aims to investigate media bias in news articles related to defense and foreign affairs by applying deep learning models and eXplainable artificial intelligence (XAI) techniques. We collected and analyzed seven, representing five major Korean media outlets, from conservative and liberal perspectives. The objective is to classify political bias and identify the specific words that contribute to this classification. We employed the BERT-base model from the Korean Language Understanding Evaluation and used local interpretable model-agnostic explanations for a comprehensive analysis. Our methodology achieved a remarkable accuracy of 98.2% in classifying the political bias of news articles, demonstrating the model’s effectiveness. The findings revealed distinct biases in coverage and statements across the media outlets: conservative outlets were more likely to emphasize threats and use singular references, while liberal outlets preferred peaceful and inclusive language. This study provides valuable insights into how the political biases of news media influence both the topics covered and the language used, even within the same category and time frame, ultimately shaping public perception.