A modular model for quality assessment in medical and nutritional journalism

Anhäuser, Marcus; Wormer, Holger; Viciano, Astrid; Rögener, Wiebke

doi:10.1007/s00103-020-03254-0

Cited by 2 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, we aim to develop a semiautomated tool that will support journalists in their daily work by evaluating the quality of their ongoing reporting and, also, by finding scientific claims in research papers and journalistic articles with a team of highly renowned medical reporters. The medical reporters evaluate the quality of medical reporting in German-speaking countries and assess the quality of print, radio, online, and TV contributions by applying a catalog of criteria in a journalistic peer review process as explained by (Anhäuser et al, 2020). The detailed criteria have been developed following the example of international research projects such as healthnewsreview.org in the USA as discussed by (Schwitzer, 2008).…”

Section: Introductionmentioning

confidence: 99%

Exploring Unsupervised Semantic Similarity Methods for Claim Verification in Health Care News Articles

Gupta,

Viciano,

Wormer

et al. 2023

Proceedings of the Conference Recent Advances in Natural Language Processing - Large Language Models for Natural Language Proce

View full text Add to dashboard Cite

In the 21st century, the proliferation of fake information has emerged as a significant threat to society. Particularly, healthcare medical reporters face challenges when verifying claims related to treatment effects, side effects, and risks mentioned in news articles, relying on scientific publications for accuracy. The accurate communication of scientific information in news articles has long been a crucial concern in the scientific community, as the dissemination of misinformation can have dire consequences in the healthcare domain. This paper delves into the application of unsupervised semantic similarity models to facilitate claim verification for medical reporters, thereby expediting the process. We explore unsupervised multilingual evidence retrieval techniques aimed at reducing the time required to obtain evidence from scientific studies. Instead of employing content classification, we propose an approach that retrieves relevant evidence from scientific publications for claim verification within the healthcare domain. Given a claim and a set of scientific publications, our system generates a list of the most similar paragraphs containing supporting evidence. Furthermore, we evaluate the performance of state-of-the-art unsupervised semantic similarity methods in this task. As the claim and evidence are present in a crosslingual space, we find that the XML-RoBERTa model exhibits high accuracy in achieving our objective.

show abstract

Section: Introductionmentioning

confidence: 99%