# BiasScanner: Automatic Detection and Classification of News Bias to Strengthen Democracy

arXiv: https://arxiv.org/abs/2407.10829 · HTML: https://mlsanity.com/paper/2407.10829/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 15 Jul 2024.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Neural Media Bias Detection Using Distant Supervision With BABE -- Bias Annotations By Experts](https://mlsanity.com/paper/2209.14557/) (2022)
- [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)
- [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)
- [Experiments in News Bias Detection with Pre-Trained Neural Transformers](https://mlsanity.com/paper/2406.09938/) (2024)

## Built on by (most cited first, 1 of 1)

- [Decoding News Bias: Multi Bias Detection in News Articles](https://mlsanity.com/paper/2501.02482/) (2025)

## Abstract (arXiv)

The increasing consumption of news online in the 21st century coincided with increased publication of disinformation, biased reporting, hate speech and other unwanted Web content. We describe BiasScanner, an application that aims to strengthen democracy by supporting news consumers with scrutinizing news articles they are reading online. BiasScanner contains a server-side pre-trained large language model to identify biased sentences of news articles and a front-end Web browser plug-in. At the time of writing, BiasScanner can identify and classify more than two dozen types of media bias at the sentence level, making it the most fine-grained model and only deployed application (automatic system in use) of its kind. It was implemented in a light-weight and privacy-respecting manner, and in addition to highlighting likely biased sentence it also provides explanations for each classification decision as well as a summary analysis for each news article. While prior research has addressed news bias detection, we are not aware of any work that resulted in a deployed browser plug-in (c.f. also biasscanner.org for a Web demo).