# Experiments in News Bias Detection with Pre-Trained Neural Transformers

arXiv: https://arxiv.org/abs/2406.09938 · HTML: https://mlsanity.com/paper/2406.09938/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 14 Jun 2024.

## Builds on (most cited first, 8 of 8)

- [MBIC -- A Media Bias Annotation Dataset Including Annotator Characteristics](https://mlsanity.com/paper/2105.11910/) (2021)
- [Sampling the News Producers: A Large News and Feature Data Set for the Study of the Complex Media Landscape](https://mlsanity.com/paper/1803.10124/) (2018)
- [FakeFlow: Fake News Detection by Modeling the Flow of Affective Information](https://mlsanity.com/paper/2101.09810/) (2021)
- [Detecting Media Bias in News Articles using Gaussian Bias Distributions](https://mlsanity.com/paper/2010.10649/) (2020)
- [Introducing MBIB -- the first Media Bias Identification Benchmark Task and Dataset Collection](https://mlsanity.com/paper/2304.13148/) (2023)
- [Towards Explainable Fact Checking](https://mlsanity.com/paper/2108.10274/) (2021)
- [Investigating Differences in Crowdsourced News Credibility Assessment: Raters, Tasks, and Expert Criteria](https://mlsanity.com/paper/2008.09533/) (2020)
- [Newsalyze: Enabling News Consumers to Understand Media Bias](https://mlsanity.com/paper/2105.09672/) (2021)

## Built on by (most cited first, 3 of 3)

- [BiasScanner: Automatic Detection and Classification of News Bias to Strengthen Democracy](https://mlsanity.com/paper/2407.10829/) (2024)
- [HierBias: Context-Conditioned Hierarchical Media Bias Detection with Multi-Task Type Classification](https://mlsanity.com/paper/2606.26100/) (2026)
- [Does Machine Translation Retain Media Bias?](https://mlsanity.com/paper/2610.19945/) (2026)

## Abstract (arXiv)

The World Wide Web provides unrivalled access to information globally, including factual news reporting and commentary. However, state actors and commercial players increasingly spread biased (distorted) or fake (non-factual) information to promote their agendas. We compare several large, pre-trained language models on the task of sentence-level news bias detection and sub-type classification, providing quantitative and qualitative results.