Can AI-Attributed News Challenge Partisan News Selection? Evidence from a Conjoint Experiment
Bibliographic Data
| ID | 12977495 |
|---|---|
| Authors | Alon Zoizner (0000-0001-5574-3475, University of Haifa, corresponding author), Jörg Matthes (0000-0001-9408-955X, University of Vienna), Nicoleta Corbu (0000-0001-9606-9827, National School of Political Science and Public Administration), Claes H De Vreese (0000-0002-4962-1698, University of Amsterdam), Frank Esser (0000-0002-1627-1521, University of Zurich), Karolina Koc-Michalska (0000-0002-5354-5616, University of Silesia in Katowice), Christian Schemer (0000-0002-7808-2240, Johannes Gutenberg University Mainz), Yannis Theocharis (0000-0001-7209-9669, Technical University of Munich), Jan Zilinsky (0000-0001-7208-8759, Technical University of Munich) |
| Year | 2025 |
| Publication date | 2025-06-29 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | The International Journal of Press/Politics (JOURNAL) |
| Journal identifiers | ISSN: 1940-1612 • E-ISSN: 1040-1620 |
| Publisher | SAGE Publishing (PUBLISHER • US) |
| DOI | 10.1177/19401612251342679 |
| OpenAlex | W4411779164 |
| Language | EN |
| References cited | 97 |
With artificial intelligence (AI) increasingly shaping newsroom practices, scholars debate how citizens perceive news attributed to algorithms versus human journalists. Yet, little is known about these preferences in today’s polarized media environment, where partisan news consumption has surged. The current study explores this issue by providing a comprehensive and systematic examination of how citizens evaluate AI-attributed news compared to human-based news from like-minded and cross-cutting partisan sources. Using a preregistered conjoint experiment in the United States ( N = 2,011) that mimics a high-choice media environment, we find that citizens evaluate AI-attributed news as negatively as cross-cutting news sources, both in terms of attitudes (perceived trustworthiness) and behavior (willingness to read the news story), while strongly preferring like-minded sources. These patterns remain stable across polarizing and non-polarizing issues and persist regardless of citizens’ preexisting attitudes toward AI, political extremity, and media trust. Our findings thus challenge more optimistic views about AI’s potential to facilitate exposure to diverse viewpoints. Moreover, they suggest that increased automation of news production faces both public mistrust and substantial reader resistance, raising concerns about the future viability of AI in journalism
Advertising · Business · Conjoint analysis · Economics · Microeconomics · Preference · Selection (genetic algorithm · Computer Science · Media Influence and Politics · Psychology · Social Media and Politics · Sports Analytics and Performance · Artificial Intelligence
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| Citation velocity | historical |
|---|---|
| Highly cited | No |