# Evaluation of Reliability Criteria for News Publishers with Large Language Models

arXiv: https://arxiv.org/abs/2412.15896 · HTML: https://mlsanity.com/paper/2412.15896/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2024.

## Built on by (most cited first, 4 of 4)

- [Assessing the Reliability of Persona-Conditioned LLMs as Synthetic Survey Respondents](https://mlsanity.com/paper/2602.18462/) (2026)
- [Evaluating the Simulation of Human Personality-Driven Susceptibility to Misinformation with LLMs](https://mlsanity.com/paper/2506.23610/) (2025)
- [Information Discernment in Large Language Models](https://mlsanity.com/paper/2607.19355/) (2026)
- [Event-Centric Human Value Understanding in News-Domain Texts: An Actor-Conditioned, Multi-Granularity Benchmark](https://mlsanity.com/paper/2603.17838/) (2026)

## Abstract (arXiv)

In this study, we investigate the use of a large language model to assist in the evaluation of the reliability of the vast number of existing online news publishers, addressing the impracticality of relying solely on human expert annotators for this task. In the context of the Italian news media market, we first task the model with evaluating expert-designed reliability criteria using a representative sample of news articles. We then compare the model's answers with those of human experts. The dataset consists of 340 news articles, each annotated by two human experts and the LLM. Six criteria are taken into account, for a total of 6,120 annotations. We observe good agreement between LLM and human annotators in three of the six evaluated criteria, including the critical ability to detect instances where a text negatively targets an entity or individual. For two additional criteria, such as the detection of sensational language and the recognition of bias in news content, LLMs generate fair annotations, albeit with certain trade-offs. Furthermore, we show that the LLM is able to help resolve disagreements among human experts, especially in tasks such as identifying cases of negative targeting.