Decoding News Bias: Multi Bias Detection in News Articles

TL;DR

This paper explores the detection of multiple biases across diverse news domains using large language models, creating a dataset and applying various detection techniques to improve news integrity.

Contribution

It introduces a comprehensive approach to detect multiple biases in news articles across various domains using LLMs and new dataset construction.

Findings

Built a large bias detection dataset using LLMs

Applied multiple bias detection techniques to news articles

Highlighted the importance of broad-spectrum bias detection

Abstract

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.