Elamur Research⌕
☰
home / papers / 2026 / AI use in American newspapers is widespread, uneven, and rarely disclosed

AI use in American newspapers is widespread, uneven, and rarely disclosed

Paper · 2026 · Jenna Russell, Marzena Karpinska, Destiny Akinode et al.

Summary

Newsrooms are adopting AI tools quickly, but no one has measured how much machine-generated text actually reaches readers of American newspapers. The authors address this with a large audit based on Pangram, a commercial AI-text detector with a very low reported false positive rate on news. Pangram labels each text as HUMAN-WRITTEN, MIXED, or AI-GENERATED.

They build two datasets:

  • recent_news: 186,507 articles from 1,528 outlets, collected via RSS between June and September 2025.
  • opinions: 44,803 op-eds from NYT, Washington Post and WSJ (2022–2025), obtained through ProQuest.

Articles are enriched with IPTC-based topic labels from Qwen3-8B, plus circulation and ownership data.

Pangram identifies about 9% of the newly published articles as at least partially or entirely written by AI. Flagging is most common in small local papers, in topics such as weather, science/technology and health, among certain owners, and in non-English content. Op-eds at the three national papers are flagged far more often than their own news coverage. Manual checks show that disclosure is rare and that flagged texts hallucinate more often.

The study matters because it offers an aggregate baseline for AI transparency in journalism and motivates concrete disclosure policies.

Key points

  • Pangram v2.0 labels in recent_news: 90.85% HUMAN-WRITTEN, 3.98% MIXED, 5.24% AI-GENERATED (9.1% "AI use" in total). In opinions, AI use is under 1%.
  • Opinion pieces at NYT, WaPo and WSJ are 6.4× more likely to be flagged than same-paper news (4.5% vs. 0.7%). Flagged authors include senators, governors, CEOs and Nobel laureates, and the rate rises from about 0% in 2022 to 3–4% in 2025.
  • Disclosure audit of 200 flagged articles: only 7 disclose AI use, and only 12 of the newspapers have public AI policies. Flagged articles are 8.2× more likely than human-labeled news to contain hallucinated claims.
  • Validation:
    • Pangram agrees with GPTZero on 88.2% of a balanced 1K-article set (Cohen's κ = 0.764).
    • Qwen3-8B topic labels match human judgments 77% of the time.
  • Boone Newsmedia and Advance Publications are among the heaviest adopters. Article links (not full texts), code and an interactive dashboard are released at ainewsaudit.github.io.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Social Sciences · 1,772 worksSafety Research · 543 worksSociology and Political Science · 577 worksCommunication · 97 works

Cite

Cite as Jenna Russell, Marzena Karpinska, Destiny Akinode, James Zhou, Katherine Thai, Bradley Emi, Max Spero and Mohit Iyyer. 2026. AI use in American newspapers is widespread, uneven, and rarely disclosed. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.663

@inproceedings{russell2026ai,
  title = {{AI use in American newspapers is widespread, uneven, and rarely disclosed}},
  author = {Jenna Russell, Marzena Karpinska, Destiny Akinode, James Zhou, Katherine Thai, Bradley Emi, Max Spero and Mohit Iyyer},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.663},
  url = {https://doi.org/10.18653/v1/2026.acl-long.663}
}

References

61 references, 9 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Abolfazl Ansari, Delvin Ce Zhang, Nafis Irtiza Tripto, and Dongwon Lee. 2025. Echoes of automation: The increasing use of llms in newsmaking. In Social, Cultural, and Behavioral Modeling: 18th International Conference, SBP-BRiMS 2025, Pittsburgh, PA, USA, October 14–17, 2025, Proceedings, page 3–13,… find
  2. Hui Bai, Jan G. Voelkel, Shane Muldowney, Johannes C. Eichstaedt, and Robb Willer. 2025. Llm-generated messages can persuade humans on policy issues. Nature Communications, 16:6037. find
  3. Adrien Barbaresi. 2021. Trafilatura: A Web Scraping Library and Command-Line Tool for Text Discovery and Extraction. In Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Proc…
  4. BBC News and European Broadcasting Union. 2025. News integrity in ai assistants. Technical report, BBC Media Centre. Accessed: 2025-10-21. find
  5. Natalie Grace Brigham, Chongjiu Gao, Tadayoshi Kohno, Franziska Roesner, and Niloofar Mireshghallah. 2024. Developing story: Case studies of generative ai’s use in journalism. Preprint, arXiv:2406.13706. — [arXiv:2406.13706](https://arxiv.org/abs/2406.13706) find
  6. Creston Brooks, Samuel Eggert, and Denis Peskoff. 2024. The rise of AI-generated content in Wikipedia. In Proceedings of the First Workshop on Advancing Natural Language Processing for Wikipedia, pages 67–79, Miami, Florida, USA. Association for Computational Linguistics. find
  7. Peter Brown and Klaudia Jaźwińska. 2025. Journalism zero: How platforms and publishers are navigating ai. Report, Tow Center for Digital Journalism, Columbia University. find
  8. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chri… — Language Models are Few-Shot Learners
  9. Roberto Cavazos and Greg Sterling. 2024. The high cost of review fraud: An economic analysis of consumer harm. https://askfortransparency.com/ research/high-cost-of-review-fraud/. Accessed: 2025-10-17. ↗
  10. Elizabeth Clark, Tal August, Sofia Serrano, Nikita Haduong, Suchin Gururangan, and Noah A. Smith. 2021. All that‘s ‘human’ is not gold: Evaluating human evaluation of generated text. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International… find
  11. Jim Conaghan. 2017. Young, old and in-between: Newspaper platform readers ages are well-distributed. https://www.newsmediaalliance.org/age-newspaper-readers-platforms/. Highlights Nielsen Scarborough USA+ 2016 Release 2 finding that the median age of daily print-newspaper readers is 57.9 years. ↗
  12. Alexander Coppock, Emily Ekins, and David Kirby. 2018. The long-lasting effects of newspaper op-eds on public opinion. Quarterly Journal of Political Science, 13(1):59–87. find
  13. Liam Dugan, Andrew Zhu, Firoj Alam, Preslav Nakov, Marianna Apidianaki, and Chris Callison-Burch. 2025. GenAI content detection task 3: Cross-domain machine generated text detection challenge. In Proceedings of the 1stWorkshop on GenAI Content Detection (GenAIDetect), pages 377–388, Abu Dhabi, UAE.… find
  14. Bradley Emi. 2025. All about false positives in ai detectors. https://www.pangram.com/blog/ all-about-false-positives-in-ai-detectors. Pangram Labs blog. ↗
  15. Bradley Emi and Max Spero. 2024. Technical Report on the Pangram AI-Generated Text Classifier. Preprint, arXiv:2402.14873. — [arXiv:2402.14873](https://arxiv.org/abs/2402.14873) find
  16. Daniel S. Evanko and Michael Di Natale. 2025. Quantifying and assessing the use of generative ai by authors and reviewers in the cancer research field. In Peer Review Congress. Accessed 2025-10-17. find
  17. Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, Md Mehrab Tanjim, Sungchul Kim, Franck Dernoncourt, Tong Yu, Ruiyi Zhang, and Nesreen K. Ahmed. 2024. Bias and fairness in large language models: A survey. Computational Linguistics, 50(3):1097– 1179.
  18. Isabel O. Gallegos, Chen Shani, Weiyan Shi, Federico Bianchi, Robb Willer, and Dan Jurafsky. 2025. AI-generated content and public persuasion: The limited effect of AI authorship labels. In NeurIPS 2024 Workshop on Regulatable ML. find
  19. Brian Jabarian and Alex Imas. 2025. Artificial writing and automated detection. find
  20. Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023a. Survey of hallucination in natural language generation. ACM Comput. Surv., 55(12).
  21. Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023b. Survey of hallucination in natural language generation. ACM Comput. Surv., 55(12).
  22. Bethany Jones and Richard Jones. 2025. Action research at the BBC: Interrogating artificial intelligence with journalists to generate actionable insights for the newsroom. Journalism, 26(8):1708–1725. Original work published 2025. find
  23. Brian Kennedy, Eileen Yam, Emma Kikuchi, Isabelle Pula, and Javier Fuentes. 2025. How americans view AI and its impact on people and society. Pew Research Center, Report. find
  24. Kate Knibbs. 2024. Ai slop is flooding medium. Wired. Accessed: 2025-10-17. find
  25. Dmitry Kobak, Rocío González-Márquez, Éva A. Horvát, and Jana Lause. 2025. Delving into llm-assisted writing in biomedical publications through excess vocabulary. Science Advances, 11(27):eadt3813. PMID: 40601754; PMCID: PMC12219543. find
  26. Daniel Chaein Lee, Jihoon Jhang, and Tae Hyun Baek. 2025. Ai-generated news content: The impact of AI writer identity and perceived AI human-likeness. International Journal of Human-Computer Interaction, pages 1–13. Published online 2025-03-24; Taylor & Francis. find
  27. Tarun Gupta and Danish Pruthi. 2025. All that glitters is not novel: Plagiarism in AI generated research. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 25721–25738, Vienna, Austria. Association for Computational Linguistics. find
  28. Weixin Liang, Zachary Izzo, Yaohui Zhang, Haley Lepp, Hancheng Cao, Xuandong Zhao, Lingjiao Chen, Haotian Ye, Sheng Liu, Zhi Huang, Daniel A. McFarland, and James Y. Zou. 2024a. Monitoring ai-modified content at scale: a case study on the impact of chatgpt on ai conference peer reviews. In Proceedin… find
  29. Hans W. A. Hanley and Zakir Durumeric. 2024. Machine-made media: Monitoring the mobilization of machine-generated articles on misinformation and mainstream news websites. Proceedings of the International AAAI Conference on Web and Social Media, 18(1):542–556. find
  30. Weixin Liang, Yaohui Zhang, Mihai Codreanu, Jiayu Wang, Hancheng Cao, and James Zou. 2025. The widespread adoption of large language model-assisted writing across society. Preprint, arXiv:2502.09747. — [arXiv:2502.09747](https://arxiv.org/abs/2502.09747) find
  31. Tony Hu, Yuliya Kyrychenko, Steve Rathje, et al. 2025. Generative language models exhibit social identity biases. Nature Computational Science, 5:65–75. find
  32. IPTC. 2025. Media topics. https://iptc.org/ standards/media-topics/. Accessed: 2025-09-29. ↗
  33. Weixin Liang, Yaohui Zhang, Zhengxuan Wu, Haley Lepp, Wenlong Ji, Xuandong Zhao, Hancheng Cao, Sheng Liu, Siyu He, Zhi Huang, Diyi Yang, Christopher Potts, Christopher D Manning, and James Y. Zou. 2024b. Mapping the increasing use of LLMs in scientific papers. In First Conference on Language Modelin… find
  34. Michael Lipka and Kirsten Eddy. 2025. Relatively few americans are getting news from AI chatbots like ChatGPT. Pew Research Center, Short Reads. find
  35. Chiara Longoni, Andrey Fradkin, Luca Cian, and Gordon Pennycook. 2022. News from generative artificial intelligence is believed less. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’22, page 97–106, New York, NY, USA. Association for Computing Machiner… find
  36. Ziming Luo, Zonglin Yang, Zexin Xu, Wei Yang, and Xinya Du. 2025. Llm4sr: A survey on large language models for scientific research. Preprint, arXiv:2501.04306. find
  37. Hana Matatov, Marianne Aubin Le Quéré, Ofra Amir, and Mor Naaman. 2025. Examining the prevalence and dynamics of ai-generated media in art subreddits. Preprint, arXiv:2410.07302. — [arXiv:2410.07302](https://arxiv.org/abs/2410.07302) find
  38. Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020a. On faithfulness and factuality in abstractive summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1906–1919, Online. Association for Computational Linguistics.
  39. Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020b. On faithfulness and factuality in abstractive summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1906–1919, Online. Association for Computational Linguistics.
  40. Medill Local News Initiative. 2024. Ai and local news: Report 2024. Technical report, Northwestern University. Accessed: 2025-10-01. find
  41. Nicole Meir. 2023. Standards around generative ai. The Definitive Source: Behind the News, The Associated Press. Accessed on 2025-10-15. find
  42. Zachary Metzger. 2024. The state of local news 2024. Technical report, Northwestern University. Accessed: 2025-10-01. find
  43. Jan Nehring, Aleksandra Gabryszak, Pascal Jür-gens, Aljoscha Burchardt, Stefan Schaffer, Matthias Spielkamp, and Birgit Stark. 2024. Large language models are echo chambers. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LRE… find
  44. Nic Newman, Amy Ross Arguedas, Craig T. Robertson, Rasmus Kleis Nielsen, and Richard Fletcher. 2025. Digital news report 2025. https://reutersinstitute.politics.ox.ac.uk/sites/default/files/2025-06/Digital_ News-Report_2025.pdf. Accessed: 2025-09-30. ↗
  45. OpenAI. 2022. Introducing ChatGPT. https://openai.com/blog/chatgpt. Accessed: 2025-10-06. ↗
  46. OpenAI. 2024. Gpt-4.1. https://platform.openai.com/docs/models/gpt-4.1. Accessed 2025. ↗
  47. Pangram. 2024. Pangram multilingual v2: Expansion of ai content detection into multiple languages. https://www.pangram.com/blog/ pangram-multilingual-v2. Pangram Labs blog. ↗
  48. Pew Research Center. 2025. News platforms (news & information) – american trends panel wave 177: Topline results. https://www.pewresearch.org/wp-content/ uploads/sites/20/2025/09/PJ_2025.09.25_ news-information-fact-sheets_topline.pdf. ↗
  49. Qwen Team. 2025. Qwen3 technical report. Preprint, arXiv:2505.09388.
  50. Damian Radcliffe. 2025. Journalism in the AI era: Opportunities and challenges in the global south and emerging economies. Trf insights report, Thomson Reuters Foundation. Survey of 200+ journalists from 70+ countries. find
  51. Jenna Russell, Marzena Karpinska, and Mohit Iyyer. 2025. People who frequently use ChatGPT for writing tasks are accurate and robust detectors of AI-generated text. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5342–5373, Vi…
  52. Fabio Salvi, Manoel Horta Ribeiro, Riccardo Gallotti, et al. 2025. On the conversational persuasiveness of gpt-4. Nature Human Behaviour, 9(10):1645–1653. find
  53. Philipp Schoenegger, Francesco Salvi, Jiacheng Liu, Xiaoli Nan, Ramit Debnath, Barbara Fasolo, Evelina Leivada, Gabriel Recchia, Fritz Günther, Ali Zarifhonarvar, Joe Kwon, Zahoor Ul Islam, Marco Dehnert, Daryl Y. H. Lee, Madeline G. Reinecke, David G. Kamper, Mert Kobaş, Adam Sandford, Jonas Kgomo… — [arXiv:2505.09662](https://arxiv.org/abs/2505.09662) find
  54. Minkyu Shin, Jin Kim, and Jiwoong Shin. 2025. The adoption and efficacy of large language models: Evidence from consumer complaints in the financial industry. Preprint, arXiv:2311.16466. — [arXiv:2311.16466](https://arxiv.org/abs/2311.16466) find
  55. Felix M. Simon. 2024. Artificial intelligence in the news: How ai retools, rationalizes, and reshapes journalism and the public arena. Report, Tow Center for Digital Journalism, Columbia University. find
  56. Felix M. Simon, Rasmus Kleis Nielsen, and Richard Fletcher. 2025. Generative ai and news report 2025: How people think about ai’s role in journalism and society. Report, Reuters Institute for the Study of Journalism, University of Oxford. find
  57. Jinyan Su, Claire Cardie, and Preslav Nakov. 2024. Adapting fake news detection to the era of large language models. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 1473–1490, Mexico City, Mexico. Association for Computational Linguistics. find
  58. Zhen Sun, Zongmin Zhang, Xinyue Shen, Ziyi Zhang, Yule Liu, Michael Backes, Yang Zhang, and Xinlei He. 2025. Are we in the AI-generated text world already? quantifying and monitoring AIGT on social media. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Vol… find
  59. Katherine Thai, Bradley Emi, Elyas Masrour, and Mohit Iyyer. 2025. Editlens: Quantifying the extent of ai editing in text. Preprint, arXiv:2510.03154. — [arXiv:2510.03154](https://arxiv.org/abs/2510.03154) find
  60. Edward Tian and Alexander Cui. 2023. GPTZero: Towards detection of AI-generated text using zero-shot and supervised methods. https://gptzero.me. Accessed: 2025-10-09. ↗
  61. Li Zhou, Ruijie Zhang, Xunlian Dai, Daniel Hershcovich, and Haizhou Li. 2025. Large language models penetration in scholarly writing and peer review. Preprint, arXiv:2502.11193. — [arXiv:2502.11193](https://arxiv.org/abs/2502.11193) find

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/russell-2026-ai-use-american-newspapers-is/