Going with the Mainstream: Exploring GPT Representation of Journalistic Culture

As generative AI becomes increasingly integrated into journalism, questions about its implications for journalistic practice grow more urgent. Despite the rise of AI systems, news organizations often lack an informed understanding of how these systems operate, potentially undermining journalists’ capacity to exercise agency in AI-assisted news production. This study explores how large language models (LLMs), such as ChatGPT, can reflect journalistic value systems. It does this by prompting the GPT-4o model to respond to survey items from the Worlds of Journalism Study that measure journalistic role perceptions, epistemologies, and ethics. These responses were compared to actual survey data from the US, UK, and Germany. Our findings suggest that GPT-4o’s outputs correspond most strongly to the survey responses of politically centrist, full-time journalists whose employers have a TV background, while showing less alignment with right-leaning, part-time, and non-degree-holding journalists. These propensities were most pronounced in the German dataset. While we do not assert that GPT-4o’s cultural orientation directly translates to its journalistic applications, the results reveal which groups of journalists’ perspectives the LLM is more—or less—likely to reflect. By mapping this alignment, the study offers an empirical point of departure for guiding how journalists can maintain agency in their use of LLMs, and thereby contributes to ongoing discourse about the ethical, epistemological, and institutional dimensions of AI in journalism.

Authors

Institutions

Publication Details

Journal
The International Journal of Press/Politics
Published
2026-05-09
DOI
https://doi.org/10.1177/19401612261442761
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Going with the Mainstream: Exploring GPT Representation of Journalistic Culture

Thomas Hanitzsch, Imke Henkel, Wiebke Loosen, Taewoo Kang et al.
The International Journal of Press/Politics
Artificial Intelligence in Healthcare and Education
article

Going with the Mainstream: Exploring GPT Representation of Journalistic Culture

Thomas Hanitzsch, Imke Henkel, Wiebke Loosen, Taewoo Kang, Sina Thäsler-Kordonouri, Tim Vos, Neil Thurman
article en

Abstract

As generative AI becomes increasingly integrated into journalism, questions about its implications for journalistic practice grow more urgent. Despite the rise of AI systems, news organizations often lack an informed understanding of how these systems operate, potentially undermining journalists’ capacity to exercise agency in AI-assisted news production. This study explores how large language models (LLMs), such as ChatGPT, can reflect journalistic value systems. It does this by prompting the GPT-4o model to respond to survey items from the Worlds of Journalism Study that measure journalistic role perceptions, epistemologies, and ethics. These responses were compared to actual survey data from the US, UK, and Germany. Our findings suggest that GPT-4o’s outputs correspond most strongly to the survey responses of politically centrist, full-time journalists whose employers have a TV background, while showing less alignment with right-leaning, part-time, and non-degree-holding journalists. These propensities were most pronounced in the German dataset. While we do not assert that GPT-4o’s cultural orientation directly translates to its journalistic applications, the results reveal which groups of journalists’ perspectives the LLM is more—or less—likely to reflect. By mapping this alignment, the study offers an empirical point of departure for guiding how journalists can maintain agency in their use of LLMs, and thereby contributes to ongoing discourse about the ethical, epistemological, and institutional dimensions of AI in journalism.

The International Journal of Press/Politics
Hans-Bredow-Institute (DE), Leeds Arts University (GB), LMU Klinikum (DE), Ludwig-Maximilians-Universität München (DE), Michigan State University (US)
Openalex Percentile: Top 8%
Artificial Intelligence in Healthcare and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.