@article{2026:schmidt:beyond_fac,
title = {Beyond fact-checking: Harnessing AI for news quality assessment},
year = {2026},
note = {Reliable scientific information is crucial for shaping public opinion
and informing economic, social, and health-related decisions. While
significant research has focused on detecting misinformation, the
automated assessment of the overall quality of journalistic content
remains largely unexplored. Addressing this gap, our study
investigates whether different artificial intelligence approaches
ranging from classical statistical methods (“bag-of-words”) to
advanced large language models (LLMs) can assist in evaluating the
quality of medical news articles based on ten established journalistic
quality criteria. Drawing on a dataset of 240 articles and
corresponding expert reviews, we show that machine learning models can
indeed assess article quality with considerable accuracy. While
several models fall short of human-level performance, fine-tuned LLMs
perform well and, in most cases, match the level of agreement with a
consolidated expert benchmark observed among individual human
reviewers. The best-performing model, a fine-tuned GPT-4o model,
detects poor-quality articles reliably when evaluated against expert
assessments (macro-averaged F1: 0.69 vs. 0.57), suggesting its
potential for use in editorial workflows, such as filtering incoming
material or double-checking an already written text. Although our
study focuses on medical journalism’s well-defined criteria, the
approach likely holds promise for enhancing quality assurance in other
areas of journalism and science communication where similar quality
criteria exist.},
journal = {SCM Studies in Communication and Media},
pages = {332--358},
author = {Schmidt, Tobias and Rieger, Jonas and Rahnenführer, Jörg and Viciano, Astrid and Wormer, Holger},
volume = {15},
number = {3}
}