Media Bias Detector - CHI 2025 - Designing and Implementing a Tool for Real-Time Selection and Framing Bias Analysis in News Coverage

Aug 20, 2026

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Mainstream media decisions on what to cover and how to frame stories can mislead readers even when content avoids outright falsehoods. This paper presents the Media Bias Detector, designed for researchers, journalists, and news consumers, combining large language models to provide near real-time, granular signals about topics, tone, political lean, and factual content at the publisher level. An expert interview study with 13 participants and a survey of 150 news consumers evaluate usability, functionality, and AI’s role, highlighting AI-enabled ways to critically engage with news in politically charged contexts.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages28

Reading Time1 h 11 min

AccessFree to download

Introduction

Selection and framing bias in mainstream news

Measuring and exposing media bias

Latest updates: h􀀍ps://dl.acm.org/doi/10.1145/3706598.3713716 RESEARCH-ARTICLE Media Bias Detector: Designing and Implementing a Tool for Real-Time Selection and Framing Bias Analysis in News Coverage JENNY S WANG, Harvard Business School, Boston, MA, United States SAMAR HAIDER, University of Pennsylvania, Philadelphia, PA, United States AMIR TOHIDI, University of Pennsylvania, Philadelphia, PA, United States ANUSHKAA GUPTA, University of Pennsylvania, Philadelphia, PA, United States YUXUAN ZHANG, University of Pennsylvania, Philadelphia, PA, United States CHRIS CALLISON-BURCH, University of Pennsylvania, Philadelphia, PA, United States View all Open Access Support provided by: University of Pennsylvania Harvard Business School Microso􀀱 Research PDF Download 3706598.3713716.pdf 18 December 2025 Total Citations: 1 Total Downloads: 3300 Published: 26 April 2025 Citation in BibTeX format CHI 2025: CHI Conference on Human Factors in Computing Systems April 26-May 1, 2025 Yokohama, Japan Conference Sponsors: SIGCHI CHI '25: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (April 2025) h􀀤ps://doi.org/10 . 1145/3706598 .3713716 ISBN: 9798400713941 . Media Bias Detector: Designing and Implementing a Tool for Real-Time Selection and Framing Bias Analysis in News Coverage Jenny S Wang∗ Harvard Business School Boston, Massachusetts, USA [jewang@hbs.edu](jewang@hbs.edu) Anushkaa Gupta University of Pennsylvania Philadelphia, Pennsylvania, USA [agupta08@sas.upenn.edu](agupta08@sas.upenn.edu) Samar Haider∗ University of Pennsylvania Philadelphia, Pennsylvania, USA [samarh@seas.upenn.edu](samarh@seas.upenn.edu) Yuxuan Zhang University of Pennsylvania Philadelphia, Pennsylvania, USA [yuxuanzh@seas.upenn.edu](yuxuanzh@seas.upenn.edu) Amir Tohidi University of Pennsylvania Philadelphia, Pennsylvania, USA [atohidi@seas.upenn.edu](atohidi@seas.upenn.edu) [Chris Callison-Burch](Chris Callison-Burch) University of Pennsylvania Philadelphia, Pennsylvania, USA[ccb@upenn.edu](ccb@upenn.edu) David Rothschild Microsoft Research New York, New York, USA [david@researchdmr.com](david@researchdmr.com) Duncan J Watts University of Pennsylvania Philadelphia, Pennsylvania, USA [djwatts@seas.upenn.edu](djwatts@seas.upenn.edu) Abstract 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. CCS Concepts • Human-centered computing → User studies; Empirical studies in HCI; • Information systems → Web applications. Keywords media bias, news analysis, large language models (LLMs), LLMdriven tools ACM Reference Format: Jenny SWang, Samar Haider, Amir Tohidi, Anushkaa Gupta, Yuxuan Zhang, Chris Callison-Burch, David Rothschild, and Duncan J Watts. 2025. Media Bias Detector: Designing and Implementing a Tool for Real-Time Selection ∗ Both authors contributed equally to this research. This work is licensed under a Creative Commons Attribution 4.0 International License. CHI’25, Yokohama, Japan © 2

FAQs

What problem does the Media Bias Detector address?

It targets subtle media bias arising from editorial decisions about selection and framing, which can mislead readers even when articles remain factually accurate.

How does the tool generate real-time insights?

It integrates large language models to provide near real-time, granular analysis of topics, tone, political leaning, and factual aspects aggregated at the publisher level.

How was the tool evaluated in the study?

The researchers interviewed 13 experts to assess usability and functionality, and they conducted a follow-up survey with 150 news consumers to further evaluate practical applications and user perspectives.

Aug 20, 2026

Upload by Mason