用大模型构建新闻多维度偏见检测数据集，提升媒体可信度。
Decoding News Bias: Multi Bias Detection in News Articles
- 基于大模型自动标注新闻中的多种偏见，覆盖多个领域。
- 构建首个跨领域新闻偏见数据集，支持多类型偏见识别。
- 适合媒体质检、内容审核及偏见研究者使用。
新闻文章为社会事件提供关键信息，但常带有各类偏见，严重扭曲公众认知并削弱对媒体的信任，因此亟需发展偏见检测技术。以往研究多集中于特定领域偏见（如政治、性别），缺乏跨领域的全面分析。大语言模型（LLMs）在自然语言理解上的优势使其成为构建数据集和检测偏见的理想工具。本文系统探索了新闻中的多种偏见，利用大模型构建了一个多领域新闻偏见数据集，并测试了多种检测方法的效果。结果表明，广谱偏见检测至关重要，本工作为提升新闻内容完整性提供了新思路与数据支持。
原文摘要 · Abstract (English)
News Articles provides crucial information about various events happening in the society but they unfortunately come with different kind of biases. These biases can significantly distort public opinion and trust in the media, making it essential to develop techniques to detect and address them. Previous works have majorly worked towards identifying biases in particular domains e.g., Political, gender biases. However, more comprehensive studies are needed to detect biases across diverse domains. Large language models (LLMs) offer a powerful way to analyze and understand natural language, making them ideal for constructing datasets and detecting these biases. In this work, we have explored various biases present in the news articles, built a dataset using LLMs and present results obtained using multiple detection techniques. Our approach highlights the importance of broad-spectrum bias detection and offers new insights for improving the integrity of news articles.
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