用大模型+多技术融合分析新闻偏见，可自动识别报道选择、措辞等偏差
Unraveling Media Perspectives: A Comprehensive Methodology Combining Large Language Models, Topic Modeling, Sentiment Analysis, and Ontology Learning to Analyse Media Bias
- 结合大模型与主题建模、情感分析、本体学习，多维度检测新闻偏见
- 在三个政治事件案例中验证了对不同层级偏见的识别能力
- 适合媒体研究者、公众及信息素养教育者使用
新闻报道偏见严重威胁理性决策与民主运行。本文提出一种可扩展、低偏见的媒体偏见分析新方法，通过自然语言处理技术，从事件选择、标签使用、用词偏好及内容遗漏等方面，系统分析政治新闻中的偏见。该方法融合层次化主题建模、情感分析与大语言模型驱动的本体学习，在三个当前政治事件案例中验证了其在多粒度层面识别新闻源偏见的有效性。研究为构建帮助公众应对复杂媒体环境的工具奠定了基础，是实现可扩展、低偏见媒体分析的重要一步。
原文摘要 · Abstract (English)
Biased news reporting poses a significant threat to informed decision-making and the functioning of democracies. This study introduces a novel methodology for scalable, minimally biased analysis of media bias in political news. The proposed approach examines event selection, labeling, word choice, and commission and omission biases across news sources by leveraging natural language processing techniques, including hierarchical topic modeling, sentiment analysis, and ontology learning with large language models. Through three case studies related to current political events, we demonstrate the methodology's effectiveness in identifying biases across news sources at various levels of granularity. This work represents a significant step towards scalable, minimally biased media bias analysis, laying the groundwork for tools to help news consumers navigate an increasingly complex media landscape.
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