Media Bias Detector: Designing and Implementing a Tool for Real-Time Selection and Framing Bias Analysis in News Coverage
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915261160882176 |
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| author | Wang, Jenny S Haider, Samar Tohidi, Amir Gupta, Anushkaa Zhang, Yuxuan Callison-Burch, Chris Rothschild, David Watts, Duncan J |
| author_facet | Wang, Jenny S Haider, Samar Tohidi, Amir Gupta, Anushkaa Zhang, Yuxuan Callison-Burch, Chris Rothschild, David Watts, Duncan J |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_06009 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Media Bias Detector: Designing and Implementing a Tool for Real-Time Selection and Framing Bias Analysis in News Coverage Wang, Jenny S Haider, Samar Tohidi, Amir Gupta, Anushkaa Zhang, Yuxuan Callison-Burch, Chris Rothschild, David Watts, Duncan J Human-Computer Interaction Computers and Society 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. |
| title | Media Bias Detector: Designing and Implementing a Tool for Real-Time Selection and Framing Bias Analysis in News Coverage |
| topic | Human-Computer Interaction Computers and Society |
| url | https://arxiv.org/abs/2502.06009 |