# Machine-Learning media bias

arXiv: https://arxiv.org/abs/2109.00024 · HTML: https://mlsanity.com/paper/2109.00024/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 36 references that are in the map.
- First posted on arXiv 31 Aug 2021.

## Builds on (most cited first, 4 of 4)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Tie-breaker: Using language models to quantify gender bias in sports journalism](https://mlsanity.com/paper/1607.03895/) (2016)
- [Using Word Embeddings to Examine Gender Bias in Dutch Newspapers, 1950-1990](https://mlsanity.com/paper/1907.08922/) (2019)
- [Finding Social Media Trolls: Dynamic Keyword Selection Methods for Rapidly-Evolving Online Debates](https://mlsanity.com/paper/1911.05332/) (2019)

## Built on by (most cited first, 6 of 6)

- [The Self-Perception and Political Biases of ChatGPT](https://mlsanity.com/paper/2304.07333/) (2023)
- [The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Bias](https://mlsanity.com/paper/2312.16148/) (2023)
- [Developing a Natural Language Understanding Model to Characterize Cable News Bias](https://mlsanity.com/paper/2310.09166/) (2023)
- [ChatGPT v.s. Media Bias: A Comparative Study of GPT-3.5 and Fine-tuned Language Models](https://mlsanity.com/paper/2403.20158/) (2024)
- [A Novel BERT-based Classifier to Detect Political Leaning of YouTube Videos based on their Titles](https://mlsanity.com/paper/2404.04261/) (2024)
- [Unraveling Media Perspectives: A Comprehensive Methodology Combining Large Language Models, Topic Modeling, Sentiment Analysis, and Ontology Learning to Analyse Media Bias](https://mlsanity.com/paper/2505.01754/) (2025)

## Abstract (arXiv)

We present an automated method for measuring media bias. Inferring which newspaper published a given article, based only on the frequencies with which it uses different phrases, leads to a conditional probability distribution whose analysis lets us automatically map newspapers and phrases into a bias space. By analyzing roughly a million articles from roughly a hundred newspapers for bias in dozens of news topics, our method maps newspapers into a two-dimensional bias landscape that agrees well with previous bias classifications based on human judgement. One dimension can be interpreted as traditional left-right bias, the other as establishment bias. This means that although news bias is inherently political, its measurement need not be.