# Media Bias Detector: Designing and Implementing a Tool for Real-Time Selection and Framing Bias Analysis in News Coverage

arXiv: https://arxiv.org/abs/2502.06009 · HTML: https://mlsanity.com/paper/2502.06009/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2025.

## Built on by (most cited first, 6 of 6)

- [Political Alignment in Large Language Models: A Multidimensional Audit of Psychometric Identity and Behavioral Bias](https://mlsanity.com/paper/2601.06194/) (2026)
- [Left, Right, or Center? Evaluating LLM Framing in News Classification and Generation](https://mlsanity.com/paper/2601.05835/) (2026)
- [Through the LLM Looking Glass: A Socratic Probing of Donkeys, Elephants, and Markets](https://mlsanity.com/paper/2503.16674/) (2025)
- [Framing Migration News with LLMs: Structured CoT as a Support for Human Interpretation](https://mlsanity.com/paper/2606.03761/) (2026)
- [HierBias: Context-Conditioned Hierarchical Media Bias Detection with Multi-Task Type Classification](https://mlsanity.com/paper/2606.26100/) (2026)
- [From Entity Mentions to Tone: An LLM-Based Pipeline for Media Bias Analysis](https://mlsanity.com/paper/2608.17454/) (2026)

## Abstract (arXiv)

Mainstream media, through their decisions on what to cover and how to frame the stories they cover, can mislead readers without using outright falsehoods. Therefore, it is crucial to have tools that expose these editorial choices underlying media bias. In this paper, we introduce the Media Bias Detector, a tool for researchers, journalists, and news consumers. By integrating large language models, we provide near real-time granular insights into the topics, tone, political lean, and facts of news articles aggregated to the publisher level. We assessed the tool's impact by interviewing 13 experts from journalism, communications, and political science, revealing key insights into usability and functionality, practical applications, and AI's role in powering media bias tools. We explored this in more depth with a follow-up survey of 150 news consumers. This work highlights opportunities for AI-driven tools that empower users to critically engage with media content, particularly in politically charged environments.