Echoes of Automation: The Increasing Use of LLMs in Newsmaking
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Abstract (arXiv)
The rapid rise of Generative AI (GenAI), particularly LLMs, poses concerns for journalistic integrity and authorship. This study examines AI-generated content across over 40,000 news articles from major, local, and college news media, in various media formats. Using three advanced AI-text detectors (e.g., Binoculars, Fast-Detect GPT, and GPTZero), we find substantial increase of GenAI use in recent years, especially in local and college news. Sentence-level analysis reveals LLMs are often used in the introduction of news, while conclusions usually written manually. Linguistic analysis shows GenAI boosts word richness and readability but lowers formality, leading to more uniform writing styles, particularly in local media.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 9 of its 20 references that are in the map. First posted on arXiv 8 Aug 2025.
Builds on
9 of 20 references.
References · 9
- 2023Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvaturecited by 287
- 2024Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Textcited by 263
- 2024Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviewscited by 164
- 2024The Ethics of ChatGPT in Medicine and Healthcare: A Systematic Review on Large Language Models (LLMs)cited by 45
- 2023Machine-Made Media: Monitoring the Mobilization of Machine-Generated Articles on Misinformation and Mainstream News Websitescited by 38
- 2024Are We in the AI-Generated Text World Already? Quantifying and Monitoring AIGT on Social Mediacited by 35
- 2023Don't lose the message while paraphrasing: A study on content preserving style transfercited by 25
- 2024Generative AI Policies under the Microscope: How CS Conferences Are Navigating the New Frontier in Scholarly Writingcited by 3
- 2025Beyond checkmate: exploring the creative chokepoints in AI textcited by 2
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.