# Decoding News Bias: Multi Bias Detection in News Articles

arXiv: https://arxiv.org/abs/2501.02482 · HTML: https://mlsanity.com/paper/2501.02482/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 5 Jan 2025.

## Builds on (most cited first, 10 of 10)

- [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)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [We Can Detect Your Bias: Predicting the Political Ideology of News Articles](https://mlsanity.com/paper/2010.05338/) (2020)
- [Disinformation Detection: An Evolving Challenge in the Age of LLMs](https://mlsanity.com/paper/2309.15847/) (2023)
- [Detecting Media Bias in News Articles using Gaussian Bias Distributions](https://mlsanity.com/paper/2010.10649/) (2020)
- [A Domain-adaptive Pre-training Approach for Language Bias Detection in News](https://mlsanity.com/paper/2205.10773/) (2022)
- [LLMs left, right, and center: Assessing GPT's capabilities to label political bias from web domains](https://mlsanity.com/paper/2407.14344/) (2024)
- [BiasScanner: Automatic Detection and Classification of News Bias to Strengthen Democracy](https://mlsanity.com/paper/2407.10829/) (2024)

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

- [Source Coverage and Citation Bias in LLM-based vs. Traditional Search Engines](https://mlsanity.com/paper/2512.09483/) (2025)

## Abstract (arXiv)

News Articles provides crucial information about various events happening in the society but they unfortunately come with different kind of biases. These biases can significantly distort public opinion and trust in the media, making it essential to develop techniques to detect and address them. Previous works have majorly worked towards identifying biases in particular domains e.g., Political, gender biases. However, more comprehensive studies are needed to detect biases across diverse domains. Large language models (LLMs) offer a powerful way to analyze and understand natural language, making them ideal for constructing datasets and detecting these biases. In this work, we have explored various biases present in the news articles, built a dataset using LLMs and present results obtained using multiple detection techniques. Our approach highlights the importance of broad-spectrum bias detection and offers new insights for improving the integrity of news articles.