In the age of information overload and polarized discourse, understanding media bias has become imperative for informed decision-making and fostering a balanced public discourse. However, without the experts' analysis, it is hard for the readers to distinguish bias from the news articles. This paper presents IndiTag, an innovative online media bias analysis system that leverages fine-grained bias indicators to dissect and distinguish bias in digital content. IndiTag offers a novel approach by incorporating large language models, bias indicators, and vector database to detect and interpret bias automatically. Complemented by a user-friendly interface facilitating automated bias analysis for readers, IndiTag offers a comprehensive platform for in-depth bias examination. We demonstrate the efficacy and versatility of IndiTag through experiments on four datasets encompassing news articles from diverse platforms. Furthermore, we discuss potential applications of IndiTag in fostering media literacy, facilitating fact-checking initiatives, and enhancing the transparency and accountability of digital media platforms. IndiTag stands as a valuable tool in the pursuit of fostering a more informed, discerning, and inclusive public discourse in the digital age. The demonstration video can be accessed from https://youtu.be/3Tux8CW46OE. We release an online system for end users and the source code is available at https://github.com/lylin0/IndiTag.

翻译：在信息过载和话语极化的时代，理解媒体偏见对于知情决策和促进平衡的公共讨论变得至关重要。然而，若无专家分析，读者很难从新闻文章中识别偏见。本文提出IndiTag，一种创新的在线媒体偏见分析系统，利用细粒度偏见指标来剖析和区分数字内容中的偏见。IndiTag通过整合大语言模型、偏见指标和向量数据库，提供了一种自动检测和解释偏见的新方法。辅以面向读者的自动化偏见分析友好界面，IndiTag为深入偏见检测提供了一个综合平台。我们通过在涵盖多个平台新闻文章的四个数据集上的实验，展示了IndiTag的有效性和多功能性。此外，我们讨论了IndiTag在提升媒体素养、辅助事实核查倡议以及增强数字媒体平台透明度和问责制方面的潜在应用。IndiTag作为推动数字时代更知情、更敏锐、更包容的公共讨论的重要工具。演示视频可从https://youtu.be/3Tux8CW46OE访问。我们发布了面向终端用户的在线系统，源代码可在https://github.com/lylin0/IndiTag获取。