IAMCR is pleased to announce the winners of the Media Production Automation Award 2026.
The award recognises outstanding academic work that advances our understanding of how automation reshapes media production practices worldwide.
The award will be formally presented at a special session during the IAMCR 2026 conference in Galway.
The winner paper is:
- "Is Human-Machine Collaborative News Credible? An Online Experimental Study on Users’ Perceptions of Credibility" by Lingyu Chu, Lingbo Tu and Hang Li.
Is Human-Machine Collaborative News Credible? An Online Experimental Study on Users’ Perceptions of Credibility
by Lingyu Chu, Lingbo Tu and Hang Li.
Submitted to the Journalism Research and Education Section
The Award Selection Committee stated:
The work of Lingyu Chu, Lingbo Tu, and Hang Li considers the automation of news production as a central issue in business strategies for production and editing. It demonstrates that the division of labor between humans and machines must be visible because adding AI-generated elements does not automatically increase credibility.
Abstract
As GenAI becomes deeply integrated into newsrooms, human-AI co-produced content is increasingly prevalent. From a user perspective, whether such news is perceived as authentic and credible remains a critical question. Drawing on the Heuristic-Systematic Model (HSM) and the Elaboration Likelihood Model (ELM), this study conducted a 2×2×3 between-subjects online experiment (n=689) to reveal the dynamic interaction mechanisms underlying perceived news credibility.
The results indicate that credibility is contextually constructed through the interplay of external cues, news quality, and individual attitudes. Specifically, (1) AI disclosure methods and news topics directly influence credibility, while news modality interacts with AI disclosure; (2) perceived expertise and readability, as two dimensions of news quality, mediate credibility through systematic and heuristic paths, respectively, while exhibiting a masking effect regarding news modality; and (3) attitudes toward AI moderate these paths, where users with positive attitudes utilize both expertise and readability to perceive credibility. This study illuminates the micro-cognitive mechanisms of trust construction during the reception of human-AI co-produced news. Furthermore, these findings provide empirical evidence for news organizations to optimize production strategies and establish sustainable trust relationships with users.
Lingyu Chu
Master student in International Journalism at the Television School, Communication University of China. Her research focuses on digital journalism, with a particular interest in how emerging technologies, especially artificial intelligence, are transforming news production, journalistic ecosystems, and everyday communication.
Lingbo Tu
Professor at the Television School, Communication University of China, where he also serves as Head of the Broadcasting and Television Department. His research focuses on digital journalism, intelligent technologies and media theory. He has published over 100 works and several books on journalism theory.
Hang Li
Ph.D. candidate at the Television School, Communication University of China. His research focuses on journalism theory, Internet governance and state-society relations in the digital age.
Media Production Automation Award Selection Committee
- Concha Edo, Chair (Complutense University of Madrid, Spain)
- Chris Paterson (University of Leeds, United Kingdom)
- María Teresa Nicolás (Universidad Panamericana, Mexico)
- Yanick Farmer (Université du Quebec à Montreal, Canada)
- Karen Arriaza Ibarra (Complutense University of Madrid, Spain)
- Pedro Jerónimo (University of Beira Interior, Portugal)